{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T06:41:31Z","timestamp":1792132891077,"version":"4.4.0"},"reference-count":279,"publisher":"Association for Computing Machinery (ACM)","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"abstract":"<jats:p>Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs). This survey thus provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts. It is organized into two main parts: (1) a detailed overview of datasets, evaluation metrics, and summarization methods before the LLM era, encompassing traditional statistical methods, deep learning approaches, and PLM fine-tuning techniques, and (2) the first detailed examination of recent advancements in benchmarking, modeling, and evaluating summarization in the LLM era. By synthesizing existing literature and presenting a cohesive overview, this survey also discusses research trends, open challenges, and proposes promising research directions in summarization, aiming to guide researchers through the evolving landscape of summarization research.<\/jats:p>","DOI":"10.1145\/3731445","type":"journal-article","created":{"date-parts":[[2025,4,19]],"date-time":"2025-04-19T06:12:06Z","timestamp":1745043126000},"update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":101,"title":["A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7017-0717","authenticated-orcid":false,"given":"Haopeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3491-5968","authenticated-orcid":false,"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago,  Chicago United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2111-7617","authenticated-orcid":false,"given":"Jiawei","family":"Zhang","sequence":"additional","affiliation":[{"name":"IFM Lab, Department of Computer Science, University of California, Davis, Davis United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,19]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Griffin Adams et\u00a0al. 2023. From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting. arXiv preprint arXiv:2309.04269(2023). arxiv:2309.04269","DOI":"10.18653\/v1\/2023.newsum-1.7"},{"key":"e_1_2_1_2_1","unstructured":"Armen Aghajanyan et\u00a0al. 2020. Better fine-tuning by reducing representational collapse. arXiv preprint arXiv:2008.03156(2020)."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 37th IEEE\/ACM International Conference on Automated Software Engineering. 1\u20135.","author":"Ahmed Toufique","year":"2022","unstructured":"Toufique Ahmed and Premkumar Devanbu. 2022. Few-shot training LLMs for project-specific code-summarization. In Proceedings of the 37th IEEE\/ACM International Conference on Automated Software Engineering. 1\u20135."},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","first-page":"1902","DOI":"10.14778\/3229863.3236220","article-title":"Sherlock: A System for Interactive Summarization of Large Text Collections.","volume":"11","author":"\u00a0al PVS","year":"2018","unstructured":"PVS Avinesh et\u00a0al. 2018. Sherlock: A System for Interactive Summarization of Large Text Collections.Proc. VLDB Endow. 11, 12 (2018), 1902\u20131905.","journal-title":"Proc. VLDB Endow."},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the 2nd Workshop on New Frontiers in Summarization. 10\u201320","author":"\u00a0al Sanghwan Bae","year":"2019","unstructured":"Sanghwan Bae et\u00a0al. 2019. Summary Level Training of Sentence Rewriting for Abstractive Summarization. In Proceedings of the 2nd Workshop on New Frontiers in Summarization. 10\u201320."},{"key":"e_1_2_1_6_1","first-page":"493","article-title":"NEWTS: A Corpus for News Topic-Focused Summarization","volume":"2022","author":"\u00a0al Seyed\u00a0Ali","year":"2022","unstructured":"Seyed\u00a0Ali Bahrainian et\u00a0al. 2022. NEWTS: A Corpus for News Topic-Focused Summarization. In Findings of the Association for Computational Linguistics: ACL 2022. 493\u2013503.","journal-title":"Findings of the Association for Computational Linguistics: ACL"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence, Vol.\u00a0 30","author":"\u00a0al Tal Baumel","year":"2016","unstructured":"Tal Baumel et\u00a0al. 2016. Topic concentration in query focused summarization datasets. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol.\u00a0 30."},{"key":"e_1_2_1_8_1","volume-title":"Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150(2020).","author":"Beltagy Iz","year":"2020","unstructured":"Iz Beltagy, Matthew\u00a0E Peters, and Arman Cohan. 2020. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150(2020)."},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of ACL-08: HLT. 197\u2013200","author":"Belz Anja","year":"2008","unstructured":"Anja Belz and Albert Gatt. 2008. Intrinsic vs. extrinsic evaluation measures for referring expression generation. In Proceedings of ACL-08: HLT. 197\u2013200."},{"key":"e_1_2_1_10_1","unstructured":"Adithya Bhaskar et\u00a0al. 2022. Zero-shot opinion summarization with GPT-3. arXiv preprint arXiv:2211.15914(2022)."},{"key":"e_1_2_1_11_1","first-page":"993","article-title":"Latent dirichlet allocation","author":"\u00a0al M Blei","year":"2003","unstructured":"David\u00a0M Blei et\u00a0al. 2003. Latent dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993\u20131022.","journal-title":"Journal of machine Learning research 3"},{"key":"e_1_2_1_12_1","volume-title":"The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems 30, 1-7","author":"Brin Sergey","year":"1998","unstructured":"Sergey Brin and Lawrence Page. 1998. The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems 30, 1-7 (1998), 107\u2013117."},{"key":"e_1_2_1_13_1","volume-title":"Language models are few-shot learners. Advances in neural information processing systems 33","author":"\u00a0al Tom Brown","year":"2020","unstructured":"Tom Brown et\u00a0al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877\u20131901."},{"key":"e_1_2_1_14_1","volume-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. 3265\u20133280","author":"\u00a0al Pengshan Cai","year":"2023","unstructured":"Pengshan Cai et\u00a0al. 2023. Generating user-engaging news headlines. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. 3265\u20133280."},{"key":"e_1_2_1_15_1","unstructured":"Daniel\u00a0O Cajueiro et\u00a0al. 2023. A comprehensive review of automatic text summarization techniques: method data evaluation and coding. arXiv preprint arXiv:2301.03403(2023)."},{"key":"e_1_2_1_16_1","unstructured":"Meng Cao. 2022. A Survey on Neural Abstractive Summarization Methods and Factual Consistency of Summarization. arXiv preprint arXiv:2204.09519(2022)."},{"key":"e_1_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Meng Cao et\u00a0al. 2020. Factual error correction for abstractive summarization models. arXiv preprint arXiv:2010.08712(2020).","DOI":"10.18653\/v1\/2020.emnlp-main.506"},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 6633\u20136649","author":"Cao Shuyang","year":"2021","unstructured":"Shuyang Cao and Lu Wang. 2021. CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 6633\u20136649."},{"key":"e_1_2_1_19_1","volume-title":"Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. 547\u2013556","author":"\u00a0al Ziqiang Cao","year":"2016","unstructured":"Ziqiang Cao et\u00a0al. 2016. AttSum: Joint Learning of Focusing and Summarization with Neural Attention. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. 547\u2013556."},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. 335\u2013336","author":"Carbonell Jaime","year":"1998","unstructured":"Jaime Carbonell and Jade Goldstein. 1998. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. 335\u2013336."},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Asli Celikyilmaz et\u00a0al. 2018. Deep communicating agents for abstractive summarization. arXiv preprint arXiv:1803.10357(2018).","DOI":"10.18653\/v1\/N18-1150"},{"key":"e_1_2_1_22_1","unstructured":"Yapei Chang et\u00a0al. 2023. A systematic exploration of book-length summarization in the era of LLMs. arXiv preprint arXiv:2310.00785(2023)."},{"key":"e_1_2_1_23_1","volume-title":"Purr: Efficiently editing language model hallucinations by denoising language model corruptions. arXiv preprint arXiv:2305.14908(2023).","author":"\u00a0al Anthony Chen","year":"2023","unstructured":"Anthony Chen et\u00a0al. 2023. Purr: Efficiently editing language model hallucinations by denoising language model corruptions. arXiv preprint arXiv:2305.14908(2023)."},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence, Vol.\u00a0 38","author":"\u00a0al Jiawei Chen","year":"2024","unstructured":"Jiawei Chen et\u00a0al. 2024. Benchmarking large language models in retrieval-augmented generation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol.\u00a0 38. 17754\u201317762."},{"key":"e_1_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Sihao Chen et\u00a0al. 2021. Improving faithfulness in abstractive summarization with contrast candidate generation and selection. arXiv preprint arXiv:2104.09061(2021).","DOI":"10.18653\/v1\/2021.naacl-main.475"},{"key":"e_1_2_1_26_1","unstructured":"Shiqi Chen et\u00a0al. 2023. Evaluating factual consistency of summaries with large language models. arXiv preprint arXiv:2305.14069(2023)."},{"key":"e_1_2_1_27_1","unstructured":"Zhiyu\u00a0Zoey Chen et\u00a0al. 2024. A Survey on Large Language Models for Critical Societal Domains: Finance Healthcare and Law. arXiv preprint arXiv:2405.01769(2024)."},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 484\u2013494","author":"Cheng Jianpeng","year":"2016","unstructured":"Jianpeng Cheng and Mirella Lapata. 2016. Neural Summarization by Extracting Sentences and Words. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 484\u2013494."},{"key":"e_1_2_1_29_1","unstructured":"Xin Cheng et\u00a0al. 2023. Towards personalized review summarization by modeling historical reviews from customer and product separately. arXiv preprint arXiv:2301.11682(2023)."},{"key":"e_1_2_1_30_1","unstructured":"Steffi Chern et\u00a0al. 2024. Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate. arXiv preprint arXiv:2401.16788(2024)."},{"key":"e_1_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Anshuman Chhabra et\u00a0al. 2024. Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias. arXiv preprint arXiv:2401.01989(2024).","DOI":"10.18653\/v1\/2024.naacl-short.1"},{"key":"e_1_2_1_32_1","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 1027\u20131038","author":"\u00a0al Sangwoo Cho","year":"2019","unstructured":"Sangwoo Cho et\u00a0al. 2019. Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 1027\u20131038."},{"key":"e_1_2_1_33_1","unstructured":"Sangwoo Cho et\u00a0al. 2021. StreamHover: Livestream transcript summarization and annotation. arXiv preprint arXiv:2109.05160(2021)."},{"key":"e_1_2_1_34_1","unstructured":"Sangwoo Cho et\u00a0al. 2022. Toward unifying text segmentation and long document summarization. arXiv preprint arXiv:2210.16422(2022)."},{"key":"e_1_2_1_35_1","volume-title":"Deep reinforcement learning from human preferences. Advances in neural information processing systems 30","author":"\u00a0al F Christiano","year":"2017","unstructured":"Paul\u00a0F Christiano et\u00a0al. 2017. Deep reinforcement learning from human preferences. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_2_1_36_1","unstructured":"Eric Chu and Peter\u00a0J Liu. 2018. Unsupervised neural multi-document abstractive summarization. arXiv preprint arXiv:1810.05739(2018)."},{"key":"e_1_2_1_37_1","first-page":"1","article-title":"Scaling instruction-finetuned language models","volume":"25","author":"\u00a0al Hyung\u00a0Won","year":"2024","unstructured":"Hyung\u00a0Won Chung et\u00a0al. 2024. Scaling instruction-finetuned language models. Journal of Machine Learning Research 25, 70 (2024), 1\u201353.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_38_1","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2748\u20132760","author":"\u00a0al Elizabeth Clark","year":"2019","unstructured":"Elizabeth Clark et\u00a0al. 2019. Sentence mover\u2019s similarity: Automatic evaluation for multi-sentence texts. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2748\u20132760."},{"key":"e_1_2_1_39_1","volume-title":"Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics. 615\u2013621","author":"\u00a0al Arman Cohan","year":"2018","unstructured":"Arman Cohan et\u00a0al. 2018. A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics. 615\u2013621."},{"key":"e_1_2_1_40_1","volume-title":"Chatlaw: Open-source legal large language model with integrated external knowledge bases. arXiv preprint arXiv:2306.16092(2023).","author":"\u00a0al Jiaxi Cui","year":"2023","unstructured":"Jiaxi Cui et\u00a0al. 2023. Chatlaw: Open-source legal large language model with integrated external knowledge bases. arXiv preprint arXiv:2306.16092(2023)."},{"key":"e_1_2_1_41_1","volume-title":"Overview of the TAC 2008 update summarization task.. In TAC.","author":"Dang Hoa\u00a0Trang","year":"2008","unstructured":"Hoa\u00a0Trang Dang, Karolina Owczarzak, et\u00a0al. 2008. Overview of the TAC 2008 update summarization task.. In TAC."},{"key":"e_1_2_1_42_1","volume-title":"Qlora: Efficient finetuning of quantized llms. Advances in Neural Information Processing Systems 36","author":"\u00a0al Tim Dettmers","year":"2024","unstructured":"Tim Dettmers et\u00a0al. 2024. Qlora: Efficient finetuning of quantized llms. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_2_1_43_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics. 4171\u20134186","author":"\u00a0al Jacob Devlin","year":"2019","unstructured":"Jacob Devlin et\u00a0al. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics. 4171\u20134186."},{"key":"e_1_2_1_44_1","volume-title":"Unified language model pre-training for natural language understanding and generation. Advances in neural information processing systems 32","author":"\u00a0al Li Dong","year":"2019","unstructured":"Li Dong et\u00a0al. 2019. Unified language model pre-training for natural language understanding and generation. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_2_1_45_1","unstructured":"Qingxiu Dong et\u00a0al. 2022. A survey on in-context learning. arXiv preprint arXiv:2301.00234(2022)."},{"key":"e_1_2_1_46_1","unstructured":"Yue Dong. 2018. A survey on neural network-based summarization methods. arXiv preprint arXiv:1804.04589(2018)."},{"key":"e_1_2_1_47_1","unstructured":"Yue Dong et\u00a0al. 2020. Discourse-aware unsupervised summarization of long scientific documents. arXiv preprint arXiv:2005.00513(2020)."},{"key":"e_1_2_1_48_1","first-page":"18","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Dong Yue","year":"2018","unstructured":"Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, and Jackie Chi\u00a0Kit Cheung. 2018. BanditSum: Extractive Summarization as a Contextual Bandit. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun\u2019ichi Tsujii (Eds.). Association for Computational Linguistics, 3739\u20133748. doi: 10.18653\/V1\/D18-1409"},{"key":"e_1_2_1_49_1","volume-title":"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics. 4830\u20134842","author":"\u00a0al Zi-Yi","year":"2021","unstructured":"Zi-Yi Dou et\u00a0al. 2021. GSum: A General Framework for Guided Neural Abstractive Summarization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics. 4830\u20134842."},{"key":"e_1_2_1_50_1","volume-title":"FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization. arXiv preprint arXiv:2005.03754(2020).","author":"\u00a0al Esin Durmus","year":"2020","unstructured":"Esin Durmus et\u00a0al. 2020. FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization. arXiv preprint arXiv:2005.03754(2020)."},{"key":"e_1_2_1_51_1","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/321510.321519","article-title":"New methods in automatic extracting","volume":"16","author":"Edmundson P","year":"1969","unstructured":"Harold\u00a0P Edmundson. 1969. New methods in automatic extracting. Journal of the ACM (JACM) 16, 2 (1969), 264\u2013285.","journal-title":"Journal of the ACM (JACM)"},{"key":"e_1_2_1_52_1","volume-title":"Automatic text summarization: A comprehensive survey. Expert systems with applications 165","author":"\u00a0al S El-Kassas","year":"2021","unstructured":"Wafaa\u00a0S El-Kassas et\u00a0al. 2021. Automatic text summarization: A comprehensive survey. Expert systems with applications 165 (2021), 113679."},{"key":"e_1_2_1_53_1","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1613\/jair.1523","article-title":"Lexrank: Graph-based lexical centrality as salience in text summarization","volume":"22","author":"Erkan G\u00fcnes","year":"2004","unstructured":"G\u00fcnes Erkan and Dragomir\u00a0R Radev. 2004. Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of artificial intelligence research 22 (2004), 457\u2013479.","journal-title":"Journal of artificial intelligence research"},{"key":"e_1_2_1_54_1","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 1074\u20131084","author":"\u00a0al Alexander\u00a0Richard","year":"2019","unstructured":"Alexander\u00a0Richard Fabbri et\u00a0al. 2019. Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 1074\u20131084."},{"key":"e_1_2_1_55_1","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1162\/tacl_a_00373","article-title":"Summeval: Re-evaluating summarization evaluation","volume":"9","author":"\u00a0al R Fabbri","year":"2021","unstructured":"Alexander\u00a0R Fabbri et\u00a0al. 2021. Summeval: Re-evaluating summarization evaluation. Transactions of the Association for Computational Linguistics 9 (2021), 391\u2013409.","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"e_1_2_1_56_1","volume-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track. 663\u2013674","author":"\u00a0al Besnik Fetahu","year":"2023","unstructured":"Besnik Fetahu et\u00a0al. 2023. InstructPTS: Instruction-Tuning LLMs for Product Title Summarization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track. 663\u2013674."},{"key":"e_1_2_1_57_1","volume-title":"GPTScore: Evaluate as You Desire. arXiv e-prints","author":"\u00a0al Jinlan Fu","year":"2023","unstructured":"Jinlan Fu et\u00a0al. 2023. GPTScore: Evaluate as You Desire. arXiv e-prints (2023), arXiv\u20132302."},{"key":"e_1_2_1_58_1","volume-title":"Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?arXiv preprint arXiv:2402","author":"\u00a0al Xue-Yong","year":"2024","unstructured":"Xue-Yong Fu et\u00a0al. 2024. Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?arXiv preprint arXiv:2402.00841(2024)."},{"key":"e_1_2_1_59_1","unstructured":"Yu Fu et\u00a0al. 2022. Inverse Reinforcement Learning for Text Summarization. arXiv preprint arXiv:2212.09917(2022)."},{"key":"e_1_2_1_60_1","unstructured":"Isabel\u00a0O Gallegos et\u00a0al. 2023. Bias and fairness in large language models: A survey. arXiv preprint arXiv:2309.00770(2023)."},{"key":"e_1_2_1_61_1","unstructured":"Mingqi Gao et\u00a0al. 2023. Human-like summarization evaluation with chatgpt. arXiv preprint arXiv:2304.02554(2023)."},{"key":"e_1_2_1_62_1","volume-title":"SUPERT: Towards new frontiers in unsupervised evaluation metrics for multi-document summarization. arXiv preprint arXiv:2005.03724(2020).","author":"\u00a0al Yang Gao","year":"2020","unstructured":"Yang Gao et\u00a0al. 2020. SUPERT: Towards new frontiers in unsupervised evaluation metrics for multi-document summarization. arXiv preprint arXiv:2005.03724(2020)."},{"key":"e_1_2_1_63_1","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 4098\u20134109","author":"\u00a0al Sebastian Gehrmann","year":"2018","unstructured":"Sebastian Gehrmann et\u00a0al. 2018. Bottom-Up Abstractive Summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 4098\u20134109."},{"key":"e_1_2_1_64_1","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 1302\u20131308","author":"Ghalandariet\u00a0al Demian\u00a0Gholipour","year":"2020","unstructured":"Demian\u00a0Gholipour Ghalandariet\u00a0al. 2020. A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 1302\u20131308."},{"key":"e_1_2_1_65_1","volume-title":"Proceedings of the 2nd Workshop on New Frontiers in Summarization. 70\u201379","author":"\u00a0al Bogdan Gliwa","year":"2019","unstructured":"Bogdan Gliwa et\u00a0al. 2019. SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization. In Proceedings of the 2nd Workshop on New Frontiers in Summarization. 70\u201379."},{"key":"e_1_2_1_66_1","volume-title":"News Summarization and Evaluation in the Era of GPT-3. arXiv e-prints","author":"\u00a0al Tanya Goyal","year":"2022","unstructured":"Tanya Goyal et\u00a0al. 2022. News Summarization and Evaluation in the Era of GPT-3. arXiv e-prints (2022), arXiv\u20132209."},{"key":"e_1_2_1_67_1","volume-title":"Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 444\u2013463","author":"\u00a0al Tanya Goyal","year":"2022","unstructured":"Tanya Goyal et\u00a0al. 2022. SNaC: Coherence Error Detection for Narrative Summarization. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 444\u2013463."},{"key":"e_1_2_1_68_1","volume-title":"Evaluating Factuality in Generation with Dependency-level Entailment. In Findings of the Association for Computational Linguistics: EMNLP","author":"Goyal Tanya","year":"2020","unstructured":"Tanya Goyal and Greg Durrett. 2020. Evaluating Factuality in Generation with Dependency-level Entailment. In Findings of the Association for Computational Linguistics: EMNLP 2020."},{"key":"e_1_2_1_69_1","volume-title":"Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics. 708\u2013719","author":"\u00a0al Max Grusky","year":"2018","unstructured":"Max Grusky et\u00a0al. 2018. Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics. 708\u2013719."},{"key":"e_1_2_1_70_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"\u00a0al Yuxian Gu","year":"2023","unstructured":"Yuxian Gu et\u00a0al. 2023. MiniLLM: Knowledge distillation of large language models. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_2_1_71_1","volume-title":"Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING","author":"Haldar Rajarshi","year":"2024","unstructured":"Rajarshi Haldar and Julia Hockenmaier. 2024. Analyzing the Performance of Large Language Models on Code Summarization. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). 995\u20131008."},{"key":"e_1_2_1_72_1","first-page":"4693","article-title":"XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages","volume":"2021","author":"\u00a0al Tahmid Hasan","year":"2021","unstructured":"Tahmid Hasan et\u00a0al. 2021. XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 4693\u20134703.","journal-title":"Findings of the Association for Computational Linguistics: ACL-IJCNLP"},{"key":"e_1_2_1_73_1","volume-title":"Teaching machines to read and comprehend. Advances in neural information processing systems 28","author":"\u00a0al Karl\u00a0Moritz","year":"2015","unstructured":"Karl\u00a0Moritz Hermann et\u00a0al. 2015. Teaching machines to read and comprehend. Advances in neural information processing systems 28 (2015)."},{"key":"e_1_2_1_74_1","volume-title":"Proceedings of the 2013 conference on empirical methods in natural language processing. 1515\u20131520","author":"\u00a0al Tsutomu Hirao","year":"2013","unstructured":"Tsutomu Hirao et\u00a0al. 2013. Single-document summarization as a tree knapsack problem. In Proceedings of the 2013 conference on empirical methods in natural language processing. 1515\u20131520."},{"key":"e_1_2_1_75_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735\u20131780."},{"key":"e_1_2_1_76_1","unstructured":"Chao-Chun Hsu and Chenhao Tan. 2021. Decision-focused summarization. arXiv preprint arXiv:2109.06896(2021)."},{"key":"e_1_2_1_77_1","volume-title":"Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685(2021).","author":"\u00a0al J Hu","year":"2021","unstructured":"Edward\u00a0J Hu et\u00a0al. 2021. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685(2021)."},{"key":"e_1_2_1_78_1","unstructured":"Kung-Hsiang Huang et\u00a0al. 2023. Embrace divergence for richer insights: A multi-document summarization benchmark and a case study on summarizing diverse information from news articles. arXiv preprint arXiv:2309.09369(2023)."},{"key":"e_1_2_1_79_1","doi-asserted-by":"crossref","unstructured":"Luyang Huang Lingfei Wu and Lu Wang. 2020. Knowledge graph-augmented abstractive summarization with semantic-driven cloze reward. arXiv preprint arXiv:2005.01159(2020).","DOI":"10.18653\/v1\/2020.acl-main.457"},{"key":"e_1_2_1_80_1","volume-title":"Multi-Dimensional Evaluation of Text Summarization with In-Context Learning. arXiv e-prints","author":"\u00a0al Sameer Jain","year":"2023","unstructured":"Sameer Jain et\u00a0al. 2023. Multi-Dimensional Evaluation of Text Summarization with In-Context Learning. arXiv e-prints (2023), arXiv\u20132306."},{"key":"e_1_2_1_81_1","first-page":"1","article-title":"Survey of hallucination in natural language generation","volume":"55","author":"\u00a0al Ziwei Ji","year":"2023","unstructured":"Ziwei Ji et\u00a0al. 2023. Survey of hallucination in natural language generation. Comput. Surveys 55, 12 (2023), 1\u201338.","journal-title":"Comput. Surveys"},{"key":"e_1_2_1_82_1","volume-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 11017\u201311031","author":"\u00a0al Qi Jia","year":"2023","unstructured":"Qi Jia et\u00a0al. 2023. Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 11017\u201311031."},{"key":"e_1_2_1_83_1","doi-asserted-by":"crossref","unstructured":"Chaoya Jiang et\u00a0al. 2023. Exploiting Pseudo Image Captions for Multimodal Summarization. arXiv preprint arXiv:2305.05496(2023).","DOI":"10.18653\/v1\/2023.findings-acl.12"},{"key":"e_1_2_1_84_1","doi-asserted-by":"crossref","unstructured":"Pengcheng Jiang et\u00a0al. 2024. TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale. arXiv preprint arXiv:2403.10351(2024).","DOI":"10.18653\/v1\/2024.naacl-long.154"},{"key":"e_1_2_1_85_1","volume-title":"CCSUM: A large-scale and high-quality dataset for abstractive news summarization.","author":"Jiang Xiang","year":"2024","unstructured":"Xiang Jiang and Markus Dreyer. 2024. CCSUM: A large-scale and high-quality dataset for abstractive news summarization. (2024)."},{"key":"e_1_2_1_86_1","volume-title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 133\u2013139","author":"\u00a0al Baoyu Jing","year":"2021","unstructured":"Baoyu Jing et\u00a0al. 2021. Multiplex Graph Neural Network for Extractive Text Summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 133\u2013139."},{"key":"e_1_2_1_87_1","unstructured":"Jaehun Jung et\u00a0al. 2023. Impossible distillation: from low-quality model to high-quality dataset & model for summarization and paraphrasing. arXiv preprint arXiv:2305.16635(2023)."},{"key":"e_1_2_1_88_1","volume-title":"ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1\u20135.","author":"\u00a0al Takatomo Kano","year":"2023","unstructured":"Takatomo Kano et\u00a0al. 2023. Speech summarization of long spoken document: Improving memory efficiency of speech\/text encoders. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1\u20135."},{"key":"e_1_2_1_89_1","volume-title":"ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences 103","author":"\u00a0al Enkelejda Kasneci","year":"2023","unstructured":"Enkelejda Kasneci et\u00a0al. 2023. ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences 103 (2023), 102274."},{"key":"e_1_2_1_90_1","volume-title":"Proceedings of NAACL-HLT. 2519\u20132531","author":"\u00a0al Byeongchang Kim","year":"2019","unstructured":"Byeongchang Kim et\u00a0al. 2019. Abstractive Summarization of Reddit Posts with Multi-level Memory Networks. In Proceedings of NAACL-HLT. 2519\u20132531."},{"key":"e_1_2_1_91_1","doi-asserted-by":"crossref","unstructured":"Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1408.5882(2014).","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_2_1_92_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Kipf N","year":"2016","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_2_1_93_1","unstructured":"Hannah\u00a0Rose Kirk et\u00a0al. 2024. The benefits risks and bounds of personalizing the alignment of large language models to individuals. Nature Machine Intelligence(2024) 1\u201310."},{"key":"e_1_2_1_94_1","volume-title":"An empirical survey on long document summarization: Datasets, models, and metrics. ACM computing surveys 55, 8","author":"\u00a0al Huan\u00a0Yee","year":"2022","unstructured":"Huan\u00a0Yee Koh et\u00a0al. 2022. An empirical survey on long document summarization: Datasets, models, and metrics. ACM computing surveys 55, 8 (2022), 1\u201335."},{"key":"e_1_2_1_95_1","first-page":"48","article-title":"BillSum: A Corpus for Automatic Summarization of US Legislation","volume":"2019","author":"Kornilova Anastassia","year":"2019","unstructured":"Anastassia Kornilova and Vlad Eidelman. 2019. BillSum: A Corpus for Automatic Summarization of US Legislation. EMNLP-IJCNLP 2019 (2019), 48.","journal-title":"EMNLP-IJCNLP"},{"key":"e_1_2_1_96_1","volume-title":"Wikihow: A large scale text summarization dataset. arXiv preprint arXiv:1810.09305(2018).","author":"Koupaee Mahnaz","year":"2018","unstructured":"Mahnaz Koupaee and William\u00a0Yang Wang. 2018. Wikihow: A large scale text summarization dataset. arXiv preprint arXiv:1810.09305(2018)."},{"key":"e_1_2_1_97_1","first-page":"910","article-title":"Mlask: multimodal summarization of video-based news articles","volume":"2023","author":"Krubi\u0144ski Mateusz","year":"2023","unstructured":"Mateusz Krubi\u0144ski and Pavel Pecina. 2023. Mlask: multimodal summarization of video-based news articles. In Findings of the Association for Computational Linguistics: EACL 2023. 910\u2013924.","journal-title":"Findings of the Association for Computational Linguistics: EACL"},{"key":"e_1_2_1_98_1","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 9332\u20139346","author":"\u00a0al Wojciech Kryscinski","year":"2020","unstructured":"Wojciech Kryscinski et\u00a0al. 2020. Evaluating the Factual Consistency of Abstractive Text Summarization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 9332\u20139346."},{"key":"e_1_2_1_99_1","doi-asserted-by":"crossref","unstructured":"Alex Kulesza et\u00a0al. 2012. Determinantal point processes for machine learning. Foundations and Trends\u00ae in Machine Learning 5 2\u20133(2012) 123\u2013286.","DOI":"10.1561\/2200000044"},{"key":"e_1_2_1_100_1","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1162\/tacl_a_00453","article-title":"SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization","volume":"10","author":"\u00a0al Philippe Laban","year":"2022","unstructured":"Philippe Laban et\u00a0al. 2022. SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization. Transactions of the Association for Computational Linguistics 10 (2022), 163\u2013177.","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"e_1_2_1_101_1","volume-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 9662\u20139676","author":"\u00a0al Philippe Laban","year":"2023","unstructured":"Philippe Laban et\u00a0al. 2023. SummEdits: Measuring LLM Ability at Factual Reasoning Through The Lens of Summarization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 9662\u20139676."},{"key":"e_1_2_1_102_1","volume-title":"Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942(2019).","author":"\u00a0al Zhenzhong Lan","year":"2019","unstructured":"Zhenzhong Lan et\u00a0al. 2019. Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942(2019)."},{"key":"e_1_2_1_103_1","doi-asserted-by":"crossref","unstructured":"Md\u00a0Tahmid\u00a0Rahman Laskar et\u00a0al. 2023. Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track. 343\u2013352.","DOI":"10.18653\/v1\/2023.emnlp-industry.33"},{"key":"e_1_2_1_104_1","first-page":"10245","article-title":"Can Large Language Models Fix Data Annotation Errors? An Empirical Study Using Debatepedia for Query-Focused Text Summarization","volume":"2023","author":"\u00a0al Tahmid\u00a0Rahman","year":"2023","unstructured":"Md\u00a0Tahmid\u00a0Rahman Laskar et\u00a0al. 2023. Can Large Language Models Fix Data Annotation Errors? An Empirical Study Using Debatepedia for Query-Focused Text Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2023. 10245\u201310255.","journal-title":"Findings of the Association for Computational Linguistics: EMNLP"},{"key":"e_1_2_1_105_1","doi-asserted-by":"crossref","unstructured":"Logan Lebanoff et\u00a0al. 2018. Adapting the neural encoder-decoder framework from single to multi-document summarization. arXiv preprint arXiv:1808.06218(2018).","DOI":"10.18653\/v1\/D18-1446"},{"key":"e_1_2_1_106_1","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2175\u20132189","author":"\u00a0al Logan Lebanoff","year":"2019","unstructured":"Logan Lebanoff et\u00a0al. 2019. Scoring sentence singletons and pairs for abstractive summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2175\u20132189."},{"key":"e_1_2_1_107_1","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 7871\u20137880","author":"\u00a0al Mike Lewis","year":"2020","unstructured":"Mike Lewis et\u00a0al. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 7871\u20137880."},{"key":"e_1_2_1_108_1","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","volume":"33","author":"\u00a0al Patrick Lewis","year":"2020","unstructured":"Patrick Lewis et\u00a0al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33 (2020), 9459\u20139474.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_109_1","unstructured":"Raymond Li et\u00a0al. 2021. Human Interpretation and Exploitation of Self-attention Patterns in Transformers: A Case Study in Extractive Summarization. CoRR abs\/2112.05364(2021). arXiv:2112.05364 https:\/\/arxiv.org\/abs\/2112.05364"},{"key":"e_1_2_1_110_1","volume-title":"Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING","author":"\u00a0al Taiji Li","year":"2024","unstructured":"Taiji Li et\u00a0al. 2024. Improving Faithfulness of Large Language Models in Summarization via Sliding Generation and Self-Consistency. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). 8804\u20138817."},{"key":"e_1_2_1_111_1","volume-title":"Loftq: Lora-fine-tuning-aware quantization for large language models. arXiv preprint arXiv:2310.08659(2023).","author":"\u00a0al Yixiao Li","year":"2023","unstructured":"Yixiao Li et\u00a0al. 2023. Loftq: Lora-fine-tuning-aware quantization for large language models. arXiv preprint arXiv:2310.08659(2023)."},{"key":"e_1_2_1_112_1","volume-title":"ISQA: Informative Factuality Feedback for Scientific Summarization. arXiv e-prints","author":"\u00a0al Zekai Li","year":"2024","unstructured":"Zekai Li et\u00a0al. 2024. ISQA: Informative Factuality Feedback for Scientific Summarization. arXiv e-prints (2024), arXiv\u20132404."},{"key":"e_1_2_1_113_1","volume-title":"Proceedings of the Association for Computational Linguistics: NAACL","author":"Li Zizhong","year":"2024","unstructured":"Zizhong Li, Haopeng Zhang, and Jiawei Zhang. 2024. Unveiling the Magic: Investigating Attention Distillation in Retrieval-augmented Generation. In Proceedings of the Association for Computational Linguistics: NAACL 2024."},{"key":"e_1_2_1_114_1","first-page":"1685","article-title":"Improving unsupervised extractive summarization with facet-aware modeling","volume":"2021","author":"\u00a0al Xinnian Liang","year":"2021","unstructured":"Xinnian Liang et\u00a0al. 2021. Improving unsupervised extractive summarization with facet-aware modeling. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 1685\u20131697.","journal-title":"Findings of the Association for Computational Linguistics: ACL-IJCNLP"},{"key":"e_1_2_1_115_1","volume-title":"Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics. 150\u2013157","author":"Lin Chin-Yew","year":"2003","unstructured":"Chin-Yew Lin and Eduard Hovy. 2003. Automatic evaluation of summaries using n-gram co-occurrence statistics. In Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics. 150\u2013157."},{"key":"e_1_2_1_116_1","volume-title":"Proceedings of the 49th annual meeting of the association for computational linguistics. 510\u2013520","author":"Lin Hui","year":"2011","unstructured":"Hui Lin and Jeff Bilmes. 2011. A class of submodular functions for document summarization. In Proceedings of the 49th annual meeting of the association for computational linguistics. 510\u2013520."},{"key":"e_1_2_1_117_1","volume-title":"Videoxum: Cross-modal visual and textural summarization of videos","author":"\u00a0al Jingyang Lin","year":"2023","unstructured":"Jingyang Lin et\u00a0al. 2023. Videoxum: Cross-modal visual and textural summarization of videos. IEEE Transactions on Multimedia(2023)."},{"key":"e_1_2_1_118_1","volume-title":"Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958(2021).","author":"\u00a0al Stephanie Lin","year":"2021","unstructured":"Stephanie Lin et\u00a0al. 2021. Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958(2021)."},{"key":"e_1_2_1_119_1","unstructured":"Fei Liu et\u00a0al. 2018. Toward abstractive summarization using semantic representations. arXiv preprint arXiv:1805.10399(2018)."},{"key":"e_1_2_1_120_1","volume-title":"Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING","author":"\u00a0al Mengsha Liu","year":"2024","unstructured":"Mengsha Liu et\u00a0al. 2024. ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). 3057\u20133074."},{"key":"e_1_2_1_121_1","volume-title":"International Conference on Learning Representations.","author":"\u00a0al J Liu","year":"2018","unstructured":"Peter\u00a0J Liu et\u00a0al. 2018. Generating Wikipedia by Summarizing Long Sequences. In International Conference on Learning Representations."},{"key":"e_1_2_1_122_1","volume-title":"Neural Abstractive Summarization for Long Text and Multiple Tables","author":"\u00a0al Shuaiqi Liu","year":"2023","unstructured":"Shuaiqi Liu et\u00a0al. 2023. Neural Abstractive Summarization for Long Text and Multiple Tables. IEEE Transactions on Knowledge and Data Engineering (2023)."},{"key":"e_1_2_1_123_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692(2019).","author":"\u00a0al Yinhan Liu","year":"2019","unstructured":"Yinhan Liu et\u00a0al. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692(2019)."},{"key":"e_1_2_1_124_1","volume-title":"HETFORMER: Heterogeneous transformer with sparse attention for long-text extractive summarization. arXiv preprint arXiv:2110.06388(2021).","author":"\u00a0al Ye Liu","year":"2021","unstructured":"Ye Liu et\u00a0al. 2021. HETFORMER: Heterogeneous transformer with sparse attention for long-text extractive summarization. arXiv preprint arXiv:2110.06388(2021)."},{"key":"e_1_2_1_125_1","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. 2890\u20132903","author":"\u00a0al Yixin Liu","year":"2022","unstructured":"Yixin Liu et\u00a0al. 2022. BRIO: Bringing Order to Abstractive Summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. 2890\u20132903."},{"key":"e_1_2_1_126_1","volume-title":"On Improving Summarization Factual Consistency from Natural Language Feedback. arXiv e-prints","author":"\u00a0al Yixin Liu","year":"2022","unstructured":"Yixin Liu et\u00a0al. 2022. On Improving Summarization Factual Consistency from Natural Language Feedback. arXiv e-prints (2022), arXiv\u20132212."},{"key":"e_1_2_1_127_1","unstructured":"Yixin Liu et\u00a0al. 2023. Benchmarking generation and evaluation capabilities of large language models for instruction controllable summarization. arXiv preprint arXiv:2311.09184(2023)."},{"key":"e_1_2_1_128_1","volume-title":"Gpteval: Nlg evaluation using gpt-4 with better human alignment. arXiv preprint arXiv:2303.16634(2023).","author":"\u00a0al Yang Liu","year":"2023","unstructured":"Yang Liu et\u00a0al. 2023. Gpteval: Nlg evaluation using gpt-4 with better human alignment. arXiv preprint arXiv:2303.16634(2023)."},{"key":"e_1_2_1_129_1","unstructured":"Yixin Liu et\u00a0al. 2023. On Learning to Summarize with Large Language Models as References. arXiv preprint arXiv:2305.14239(2023)."},{"key":"e_1_2_1_130_1","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 5070","author":"Liu Yang","year":"2019","unstructured":"Yang Liu and Mirella Lapata. 2019. Hierarchical Transformers for Multi-Document Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 5070."},{"key":"e_1_2_1_131_1","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. 3730\u20133740","author":"Liu Yang","year":"2019","unstructured":"Yang Liu and Mirella Lapata. 2019. Text Summarization with Pretrained Encoders. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. 3730\u20133740."},{"key":"e_1_2_1_132_1","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. 1065\u20131072","author":"Liu Yixin","year":"2021","unstructured":"Yixin Liu and Pengfei Liu. 2021. SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. 1065\u20131072."},{"key":"e_1_2_1_133_1","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 8068\u20138074","author":"Lu Yao","year":"2020","unstructured":"Yao Lu, Yue Dong, and Laurent Charlin. 2020. Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 8068\u20138074."},{"key":"e_1_2_1_134_1","volume-title":"The automatic creation of literature abstracts. IBM Journal of research and development 2, 2","author":"Luhn Hans\u00a0Peter","year":"1958","unstructured":"Hans\u00a0Peter Luhn. 1958. The automatic creation of literature abstracts. IBM Journal of research and development 2, 2 (1958), 159\u2013165."},{"key":"e_1_2_1_135_1","volume-title":"ChatGPT as a Factual Inconsistency Evaluator for Abstractive Text Summarization. arXiv e-prints","author":"\u00a0al Zheheng Luo","year":"2023","unstructured":"Zheheng Luo et\u00a0al. 2023. ChatGPT as a Factual Inconsistency Evaluator for Abstractive Text Summarization. arXiv e-prints (2023), arXiv\u20132303."},{"key":"e_1_2_1_136_1","unstructured":"Qing Lyu et\u00a0al. 2024. Towards faithful model explanation in nlp: A survey. Computational Linguistics(2024) 1\u201370."},{"key":"e_1_2_1_137_1","unstructured":"Chong Ma et\u00a0al. 2023. ImpressionGPT: an iterative optimizing framework for radiology report summarization with chatGPT. arXiv preprint arXiv:2304.08448(2023)."},{"key":"e_1_2_1_138_1","volume-title":"Llm-pruner: On the structural pruning of large language models. Advances in neural information processing systems 36","author":"\u00a0al Xinyin Ma","year":"2023","unstructured":"Xinyin Ma et\u00a0al. 2023. Llm-pruner: On the structural pruning of large language models. Advances in neural information processing systems 36 (2023), 21702\u201321720."},{"key":"e_1_2_1_139_1","volume-title":"Advances in automatic text summarization","author":"Mani Inderjeet","unstructured":"Inderjeet Mani and Mark\u00a0T Maybury. 1999. Advances in automatic text summarization. MIT press."},{"key":"e_1_2_1_140_1","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1109\/TASLP.2022.3161157","article-title":"Fact-driven abstractive summarization by utilizing multi-granular multi-relational knowledge","volume":"30","author":"\u00a0al Qianren Mao","year":"2022","unstructured":"Qianren Mao et\u00a0al. 2022. Fact-driven abstractive summarization by utilizing multi-granular multi-relational knowledge. IEEE\/ACM Transactions on Audio, Speech, and Language Processing 30 (2022), 1665\u20131678.","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"key":"e_1_2_1_141_1","doi-asserted-by":"crossref","unstructured":"Joshua Maynez et\u00a0al. 2020. On faithfulness and factuality in abstractive summarization. arXiv preprint arXiv:2005.00661(2020).","DOI":"10.18653\/v1\/2020.acl-main.173"},{"key":"e_1_2_1_142_1","volume-title":"European Conference on Information Retrieval. Springer, 557\u2013564","author":"McDonald Ryan","year":"2007","unstructured":"Ryan McDonald. 2007. A study of global inference algorithms in multi-document summarization. In European Conference on Information Retrieval. Springer, 557\u2013564."},{"key":"e_1_2_1_143_1","volume-title":"Proceedings of the 2004 conference on empirical methods in natural language processing. 404\u2013411","author":"Mihalcea Rada","year":"2004","unstructured":"Rada Mihalcea and Paul Tarau. 2004. Textrank: Bringing order into text. In Proceedings of the 2004 conference on empirical methods in natural language processing. 404\u2013411."},{"key":"e_1_2_1_144_1","unstructured":"Tomas Mikolov et\u00a0al. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781(2013)."},{"key":"e_1_2_1_145_1","first-page":"10002","article-title":"LLM aided semi-supervision for efficient Extractive Dialog Summarization","volume":"2023","author":"\u00a0al Nishant Mishra","year":"2023","unstructured":"Nishant Mishra et\u00a0al. 2023. LLM aided semi-supervision for efficient Extractive Dialog Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2023. 10002\u201310009.","journal-title":"Findings of the Association for Computational Linguistics: EMNLP"},{"key":"e_1_2_1_146_1","doi-asserted-by":"crossref","first-page":"156043","DOI":"10.1109\/ACCESS.2021.3129786","article-title":"A survey of automatic text summarization: Progress, process and challenges","volume":"9","author":"\u00a0al F Mridha","year":"2021","unstructured":"Muhammad\u00a0F Mridha et\u00a0al. 2021. A survey of automatic text summarization: Progress, process and challenges. IEEE Access 9(2021), 156043\u2013156070.","journal-title":"IEEE Access"},{"key":"e_1_2_1_147_1","first-page":"387","article-title":"Topic-aware multimodal summarization","volume":"2022","author":"\u00a0al Sourajit Mukherjee","year":"2022","unstructured":"Sourajit Mukherjee et\u00a0al. 2022. Topic-aware multimodal summarization. In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022. 387\u2013398.","journal-title":"Findings of the Association for Computational Linguistics: AACL-IJCNLP"},{"key":"e_1_2_1_148_1","first-page":"4162","article-title":"Using LLM for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries","volume":"2023","author":"\u00a0al Nishanth Nakshatri","year":"2023","unstructured":"Nishanth Nakshatri et\u00a0al. 2023. Using LLM for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries. In Findings of the Association for Computational Linguistics: EMNLP 2023. 4162\u20134173.","journal-title":"Findings of the Association for Computational Linguistics: EMNLP"},{"key":"e_1_2_1_149_1","volume-title":"Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning. Association for Computational Linguistics","author":"\u00a0al Ramesh Nallapati","year":"2016","unstructured":"Ramesh Nallapati et\u00a0al. 2016. Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning. Association for Computational Linguistics, Berlin, Germany, 280\u2013290."},{"key":"e_1_2_1_150_1","volume-title":"Thirty-first AAAI conference on artificial intelligence.","author":"\u00a0al Ramesh Nallapati","year":"2017","unstructured":"Ramesh Nallapati et\u00a0al. 2017. Summarunner: A recurrent neural network based sequence model for extractive summarization of documents. In Thirty-first AAAI conference on artificial intelligence."},{"key":"e_1_2_1_151_1","doi-asserted-by":"crossref","unstructured":"Feng Nan et\u00a0al. 2021. Entity-level factual consistency of abstractive text summarization. arXiv preprint arXiv:2102.09130(2021).","DOI":"10.18653\/v1\/2021.eacl-main.235"},{"key":"e_1_2_1_152_1","volume-title":"2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 1797\u20131807","author":"\u00a0al Shashi Narayan","year":"2018","unstructured":"Shashi Narayan et\u00a0al. 2018. Don\u2019t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization. In 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 1797\u20131807."},{"key":"e_1_2_1_153_1","volume-title":"Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics. 1747\u20131759","author":"\u00a0al Shashi Narayan","year":"2018","unstructured":"Shashi Narayan et\u00a0al. 2018. Ranking Sentences for Extractive Summarization with Reinforcement Learning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics. 1747\u20131759."},{"key":"e_1_2_1_154_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3597307","article-title":"Biases in large language models: origins, inventory, and discussion","volume":"15","author":"\u00a0al Roberto Navigli","year":"2023","unstructured":"Roberto Navigli et\u00a0al. 2023. Biases in large language models: origins, inventory, and discussion. ACM Journal of Data and Information Quality 15, 2 (2023), 1\u201321.","journal-title":"ACM Journal of Data and Information Quality"},{"key":"e_1_2_1_155_1","unstructured":"Marcel Nawrath et\u00a0al. 2024. On the Role of Summary Content Units in Text Summarization Evaluation. arXiv preprint arXiv:2404.01701(2024)."},{"key":"e_1_2_1_156_1","volume-title":"Proceedings of the human language technology conference of the north american chapter of the association for computational linguistics. 145\u2013152","author":"Nenkova Ani","year":"2004","unstructured":"Ani Nenkova and Rebecca\u00a0J Passonneau. 2004. Evaluating content selection in summarization: The pyramid method. In Proceedings of the human language technology conference of the north american chapter of the association for computational linguistics. 145\u2013152."},{"key":"e_1_2_1_158_1","volume-title":"Training language models to follow instructions with human feedback. Advances in neural information processing systems 35","author":"\u00a0al Long Ouyang","year":"2022","unstructured":"Long Ouyang et\u00a0al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems 35 (2022), 27730\u201327744."},{"key":"e_1_2_1_159_1","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1016\/j.ipm.2007.01.019","article-title":"DUC in context","volume":"43","author":"Over Paul","year":"2007","unstructured":"Paul Over, Hoa Dang, and Donna Harman. 2007. DUC in context. Information Processing & Management 43, 6 (2007), 1506\u20131520.","journal-title":"Information Processing & Management"},{"key":"e_1_2_1_160_1","volume-title":"International Conference on Learning Representations.","author":"\u00a0al Romain Paulus","year":"2018","unstructured":"Romain Paulus et\u00a0al. 2018. A Deep Reinforced Model for Abstractive Summarization. In International Conference on Learning Representations."},{"key":"e_1_2_1_161_1","volume-title":"Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 1532\u20131543","author":"\u00a0al Jeffrey Pennington","year":"2014","unstructured":"Jeffrey Pennington et\u00a0al. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 1532\u20131543."},{"key":"e_1_2_1_162_1","volume-title":"Proceedings of the Workshop on New Frontiers in Summarization. 74\u201384","author":"\u00a0al Maxime Peyrard","year":"2017","unstructured":"Maxime Peyrard et\u00a0al. 2017. Learning to score system summaries for better content selection evaluation.. In Proceedings of the Workshop on New Frontiers in Summarization. 74\u201384."},{"key":"e_1_2_1_163_1","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1080\/15265161.2023.2233356","article-title":"AUTOGEN: A personalized large language model for academic enhancement\u2014Ethics and proof of principle","volume":"23","author":"\u00a0al Sebastian","year":"2023","unstructured":"Sebastian Porsdam\u00a0Mann et\u00a0al. 2023. AUTOGEN: A personalized large language model for academic enhancement\u2014Ethics and proof of principle. The American Journal of Bioethics 23, 10 (2023), 28\u201341.","journal-title":"The American Journal of Bioethics"},{"key":"e_1_2_1_164_1","unstructured":"Dongqi Pu and Vera Demberg. 2023. ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer. arXiv preprint arXiv:2306.07799(2023)."},{"key":"e_1_2_1_165_1","unstructured":"Dongqi Pu and Vera Demberg. 2024. RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization. arXiv preprint arXiv:2405.00657(2024)."},{"key":"e_1_2_1_166_1","unstructured":"Xiao Pu et\u00a0al. 2023. Summarization is (almost) dead. arXiv preprint arXiv:2309.09558(2023)."},{"key":"e_1_2_1_167_1","first-page":"2401","article-title":"ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training","volume":"2020","author":"\u00a0al Weizhen Qi","year":"2020","unstructured":"Weizhen Qi et\u00a0al. 2020. ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training. In Findings of the Association for Computational Linguistics: EMNLP 2020. 2401\u20132410.","journal-title":"Findings of the Association for Computational Linguistics: EMNLP"},{"key":"e_1_2_1_168_1","volume-title":"Proceedings of the 28th ACM International Conference on Multimedia. 1047\u20131055","author":"\u00a0al Leigang Qu","year":"2020","unstructured":"Leigang Qu et\u00a0al. 2020. Context-aware multi-view summarization network for image-text matching. In Proceedings of the 28th ACM International Conference on Multimedia. 1047\u20131055."},{"key":"e_1_2_1_169_1","unstructured":"Alec Radford et\u00a0al. 2018. Improving language understanding by generative pre-training. (2018)."},{"key":"e_1_2_1_170_1","first-page":"5485","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"\u00a0al Colin Raffel","year":"2020","unstructured":"Colin Raffel et\u00a0al. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21, 1 (2020), 5485\u20135551.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_2_1_171_1","unstructured":"Mathieu Ravaut et\u00a0al. 2023. On Position Bias in Summarization with Large Language Models. arXiv preprint arXiv:2310.10570(2023)."},{"key":"e_1_2_1_172_1","volume-title":"Promptsum: Parameter-efficient controllable abstractive summarization. arXiv preprint arXiv:2308.03117(2023).","author":"\u00a0al Mathieu Ravaut","year":"2023","unstructured":"Mathieu Ravaut et\u00a0al. 2023. Promptsum: Parameter-efficient controllable abstractive summarization. arXiv preprint arXiv:2308.03117(2023)."},{"key":"e_1_2_1_173_1","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. 3982\u20133992","author":"Reimers Nils","year":"2019","unstructured":"Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. 3982\u20133992."},{"key":"e_1_2_1_174_1","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 379\u2013389","author":"\u00a0al M Rush","year":"2015","unstructured":"Alexander\u00a0M Rush et\u00a0al. 2015. A Neural Attention Model for Abstractive Sentence Summarization. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 379\u2013389."},{"key":"e_1_2_1_175_1","volume-title":"Proceedings of the 29th International Conference on Computational Linguistics. 6139\u20136150","author":"Salchner Marco\u00a0Ferdinand","year":"2022","unstructured":"Marco\u00a0Ferdinand Salchner and Adam Jatowt. 2022. A Survey of Automatic Text Summarization Using Graph Neural Networks. In Proceedings of the 29th International Conference on Computational Linguistics. 6139\u20136150."},{"key":"e_1_2_1_176_1","first-page":"e26752","article-title":"The new york times annotated corpus","volume":"6","author":"Sandhaus Evan","year":"2008","unstructured":"Evan Sandhaus. 2008. The new york times annotated corpus. Linguistic Data Consortium, Philadelphia 6, 12 (2008), e26752.","journal-title":"Linguistic Data Consortium, Philadelphia"},{"key":"e_1_2_1_177_1","unstructured":"Victor Sanh et\u00a0al. 2019. DistilBERT a distilled version of BERT: smaller faster cheaper and lighter. arXiv preprint arXiv:1910.01108(2019)."},{"key":"e_1_2_1_178_1","doi-asserted-by":"crossref","unstructured":"Ritesh Sarkhel et\u00a0al. 2020. Interpretable Multi-Headed Attention for Abstractive Summarization at Controllable Lengths. arxiv:2002.07845 \u00a0[cs.CL]","DOI":"10.18653\/v1\/2020.coling-main.606"},{"key":"e_1_2_1_179_1","doi-asserted-by":"crossref","unstructured":"Thomas Scialom et\u00a0al. 2019. Answers unite! unsupervised metrics for reinforced summarization models. arXiv preprint arXiv:1909.01610(2019).","DOI":"10.18653\/v1\/D19-1320"},{"key":"e_1_2_1_180_1","volume-title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 6594\u20136604","author":"\u00a0al Thomas Scialom","year":"2021","unstructured":"Thomas Scialom et\u00a0al. 2021. QuestEval: Summarization Asks for Fact-based Evaluation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 6594\u20136604."},{"key":"e_1_2_1_181_1","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 1073\u20131083","year":"2017","unstructured":"Abigail See et\u00a0al. 2017. Get To The Point: Summarization with Pointer-Generator Networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 1073\u20131083."},{"key":"e_1_2_1_182_1","volume-title":"Overview of the TREC 2018 Real-Time Summarization Track.. In TREC.","author":"\u00a0al Royal Sequiera","year":"2018","unstructured":"Royal Sequiera et\u00a0al. 2018. Overview of the TREC 2018 Real-Time Summarization Track.. In TREC."},{"key":"e_1_2_1_183_1","doi-asserted-by":"crossref","unstructured":"Chantal Shaib et\u00a0al. 2023. Summarizing simplifying and synthesizing medical evidence using gpt-3 (with varying success). arXiv preprint arXiv:2305.06299(2023).","DOI":"10.18653\/v1\/2023.acl-short.119"},{"key":"e_1_2_1_184_1","volume-title":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 109\u2013114","author":"\u00a0al Ori Shapira","year":"2017","unstructured":"Ori Shapira et\u00a0al. 2017. Interactive abstractive summarization for event news tweets. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 109\u2013114."},{"key":"e_1_2_1_185_1","volume-title":"Proceedings of the 2021 conference of the North American Chapter of the Association for Computational Linguistics. 657\u2013677","author":"\u00a0al Ori Shapira","year":"2021","unstructured":"Ori Shapira et\u00a0al. 2021. Extending multi-document summarization evaluation to the interactive setting. In Proceedings of the 2021 conference of the North American Chapter of the Association for Computational Linguistics. 657\u2013677."},{"key":"e_1_2_1_186_1","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2204\u20132213","author":"Sharma Eva","year":"2019","unstructured":"Eva Sharma, Chen Li, and Lu Wang. 2019. BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2204\u20132213."},{"key":"e_1_2_1_187_1","first-page":"4215","article-title":"Large language models are not yet human-level evaluators for abstractive summarization","volume":"2023","author":"\u00a0al Chenhui Shen","year":"2023","unstructured":"Chenhui Shen et\u00a0al. 2023. Large language models are not yet human-level evaluators for abstractive summarization. In Findings of the Association for Computational Linguistics: EMNLP 2023. 4215\u20134233.","journal-title":"Findings of the Association for Computational Linguistics: EMNLP"},{"key":"e_1_2_1_188_1","unstructured":"Noah Shinn et\u00a0al. 2023. Reflexion: an autonomous agent with dynamic memory and self-reflection. arXiv preprint arXiv:2303.11366(2023)."},{"key":"e_1_2_1_189_1","unstructured":"Kaiqiang Song et\u00a0al. 2018. Structure-infused copy mechanisms for abstractive summarization. arXiv preprint arXiv:1806.05658(2018)."},{"key":"e_1_2_1_190_1","volume-title":"International Conference on Machine Learning. PMLR, 5926\u20135936","author":"\u00a0al Kaitao Song","year":"2019","unstructured":"Kaitao Song et\u00a0al. 2019. MASS: Masked Sequence to Sequence Pre-training for Language Generation. In International Conference on Machine Learning. PMLR, 5926\u20135936."},{"key":"e_1_2_1_191_1","unstructured":"Mingjie Sun et\u00a0al. 2023. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695(2023)."},{"key":"e_1_2_1_192_1","unstructured":"Shichao Sun et\u00a0al. 2024. Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization. arXiv preprint arXiv:2406.00507(2024)."},{"key":"e_1_2_1_193_1","unstructured":"Shichao Sun and Wenjie Li. 2021. Alleviating exposure bias via contrastive learning for abstractive text summarization. arXiv preprint arXiv:2108.11846(2021)."},{"key":"e_1_2_1_194_1","unstructured":"Weisong Sun et\u00a0al. 2023. Automatic Code Summarization via ChatGPT: How Far Are We?arXiv preprint arXiv:2305.12865(2023)."},{"key":"e_1_2_1_195_1","volume-title":"Sequence to sequence learning with neural networks. Advances in neural information processing systems 27","author":"\u00a0al Ilya Sutskever","year":"2014","unstructured":"Ilya Sutskever et\u00a0al. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014)."},{"key":"e_1_2_1_196_1","volume-title":"Proceedings of the 12th Conference of the European Chapter of the ACL (EACL","author":"Takamura Hiroya","year":"2009","unstructured":"Hiroya Takamura and Manabu Okumura. 2009. Text summarization model based on maximum coverage problem and its variant. In Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009). 781\u2013789."},{"key":"e_1_2_1_197_1","first-page":"5220","article-title":"Evaluating the factual consistency of large language models through news summarization","volume":"2023","author":"\u00a0al Derek Tam","year":"2023","unstructured":"Derek Tam et\u00a0al. 2023. Evaluating the factual consistency of large language models through news summarization. In Findings of the Association for Computational Linguistics: ACL 2023. 5220\u20135255.","journal-title":"Findings of the Association for Computational Linguistics: ACL"},{"key":"e_1_2_1_198_1","unstructured":"Liyan Tang et\u00a0al. [n.\u00a0d.]. TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization. arXiv preprint arXiv:2402.13249([n.\u00a0d.])."},{"key":"e_1_2_1_199_1","volume-title":"Evaluating large language models on medical evidence summarization. npj Digital Medicine 6, 1","author":"\u00a0al Liyan Tang","year":"2023","unstructured":"Liyan Tang et\u00a0al. 2023. Evaluating large language models on medical evidence summarization. npj Digital Medicine 6, 1 (2023), 158."},{"key":"e_1_2_1_200_1","unstructured":"Yi Tay et\u00a0al. 2022. Ul2: Unifying language learning paradigms. arXiv preprint arXiv:2205.05131(2022)."},{"key":"e_1_2_1_201_1","unstructured":"Gemini Team et\u00a0al. 2023. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805(2023)."},{"key":"e_1_2_1_202_1","volume-title":"Large language models in medicine. Nature medicine 29, 8","author":"\u00a0al Arun\u00a0James","year":"2023","unstructured":"Arun\u00a0James Thirunavukarasu et\u00a0al. 2023. Large language models in medicine. Nature medicine 29, 8 (2023), 1930\u20131940."},{"key":"e_1_2_1_203_1","volume-title":"Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971(2023).","author":"\u00a0al Hugo Touvron","year":"2023","unstructured":"Hugo Touvron et\u00a0al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971(2023)."},{"key":"e_1_2_1_204_1","volume-title":"Clinical text summarization: Adapting large language models can outperform human experts. Research Square","author":"\u00a0al Dave","year":"2023","unstructured":"Dave Van\u00a0Veen et\u00a0al. 2023. Clinical text summarization: Adapting large language models can outperform human experts. Research Square (2023)."},{"key":"e_1_2_1_205_1","volume-title":"Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems. 11\u201320","author":"\u00a0al Oleg Vasilyev","year":"2020","unstructured":"Oleg Vasilyev et\u00a0al. 2020. Fill in the BLANC: Human-free quality estimation of document summaries. In Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems. 11\u201320."},{"key":"e_1_2_1_206_1","unstructured":"Ashish Vaswani et\u00a0al. 2017. Attention is all you need. In Advances in neural information processing systems. 5998\u20136008."},{"key":"e_1_2_1_207_1","volume-title":"Graph Attention Networks. In International Conference on Learning Representations.","author":"\u00a0al Petar Veli\u010dkovi\u0107","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107 et\u00a0al. 2018. Graph Attention Networks. In International Conference on Learning Representations."},{"key":"e_1_2_1_208_1","volume-title":"Pointer networks. Advances in neural information processing systems 28","author":"\u00a0al Oriol Vinyals","year":"2015","unstructured":"Oriol Vinyals et\u00a0al. 2015. Pointer networks. Advances in neural information processing systems 28 (2015)."},{"key":"e_1_2_1_209_1","volume-title":"Proceedings of the 33rd ACM\/IEEE international conference on automated software engineering. 397\u2013407","author":"\u00a0al Yao Wan","year":"2018","unstructured":"Yao Wan et\u00a0al. 2018. Improving automatic source code summarization via deep reinforcement learning. In Proceedings of the 33rd ACM\/IEEE international conference on automated software engineering. 397\u2013407."},{"key":"e_1_2_1_210_1","doi-asserted-by":"crossref","unstructured":"Alex Wang et\u00a0al. 2020. Asking and answering questions to evaluate the factual consistency of summaries. arXiv preprint arXiv:2004.04228(2020).","DOI":"10.18653\/v1\/2020.acl-main.450"},{"key":"e_1_2_1_211_1","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 6209\u20136219","author":"\u00a0al Danqing Wang","year":"2020","unstructured":"Danqing Wang et\u00a0al. 2020. Heterogeneous Graph Neural Networks for Extractive Document Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 6209\u20136219."},{"key":"e_1_2_1_212_1","doi-asserted-by":"crossref","unstructured":"Fei Wang et\u00a0al. 2022. Salience allocation as guidance for abstractive summarization. arXiv preprint arXiv:2210.12330(2022).","DOI":"10.18653\/v1\/2022.emnlp-main.409"},{"key":"e_1_2_1_213_1","volume-title":"et\u00a0al","author":"Wang Jiaan","year":"2023","unstructured":"Jiaan Wang,, et\u00a0al. 2023. Cross-lingual summarization via chatgpt. arXiv preprint arXiv:2302.14229(2023)."},{"key":"e_1_2_1_214_1","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/TSE.2020.2979701","article-title":"Reinforcement-learning-guided source code summarization using hierarchical attention","volume":"48","author":"Wang Wenhua","year":"2020","unstructured":"Wenhua Wang,, et\u00a0al. 2020. Reinforcement-learning-guided source code summarization using hierarchical attention. IEEE Transactions on software Engineering 48, 1 (2020), 102\u2013119.","journal-title":"IEEE Transactions on software Engineering"},{"key":"e_1_2_1_215_1","volume-title":"Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method. arXiv e-prints","author":"\u00a0al Yiming Wang","year":"2023","unstructured":"Yiming Wang et\u00a0al. 2023. Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method. arXiv e-prints (2023), arXiv\u20132305."},{"key":"e_1_2_1_216_1","unstructured":"Jason Wei et\u00a0al. 2022. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In Advances in Neural Information Processing Systems."},{"key":"e_1_2_1_217_1","volume-title":"Parameter-Efficient Multilingual Summarisation: An Empirical Study. arXiv e-prints","author":"\u00a0al Chenxi Whitehouse","year":"2023","unstructured":"Chenxi Whitehouse et\u00a0al. 2023. Parameter-Efficient Multilingual Summarisation: An Empirical Study. arXiv e-prints (2023), arXiv\u20132311."},{"key":"e_1_2_1_218_1","doi-asserted-by":"crossref","first-page":"100070","DOI":"10.1016\/j.nlp.2024.100070","article-title":"A survey of text summarization: Techniques, evaluation and challenges","volume":"7","author":"Wibawa Aji\u00a0Prasetya","year":"2024","unstructured":"Aji\u00a0Prasetya Wibawa, Fachrul Kurniawan, et\u00a0al. 2024. A survey of text summarization: Techniques, evaluation and challenges. Natural Language Processing Journal 7 (2024), 100070.","journal-title":"Natural Language Processing Journal"},{"key":"e_1_2_1_219_1","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1007\/s10514-023-10139-z","article-title":"Tidybot: Personalized robot assistance with large language models","volume":"47","author":"\u00a0al Jimmy Wu","year":"2023","unstructured":"Jimmy Wu et\u00a0al. 2023. Tidybot: Personalized robot assistance with large language models. Autonomous Robots 47, 8 (2023), 1087\u20131102.","journal-title":"Autonomous Robots"},{"key":"e_1_2_1_220_1","volume-title":"Large Language Models are Diverse Role-Players for Summarization Evaluation. arXiv e-prints","author":"\u00a0al Ning Wu","year":"2023","unstructured":"Ning Wu et\u00a0al. 2023. Large Language Models are Diverse Role-Players for Summarization Evaluation. arXiv e-prints (2023), arXiv\u20132303."},{"key":"e_1_2_1_221_1","volume-title":"Less is More for Long Document Summary Evaluation by LLMs. arXiv e-prints","author":"\u00a0al Yunshu Wu","year":"2023","unstructured":"Yunshu Wu et\u00a0al. 2023. Less is More for Long Document Summary Evaluation by LLMs. arXiv e-prints (2023), arXiv\u20132309."},{"key":"e_1_2_1_222_1","volume-title":"Hallucination Diversity-Aware Active Learning for Text Summarization. arXiv e-prints","author":"\u00a0al Yu Xia","year":"2024","unstructured":"Yu Xia et\u00a0al. 2024. Hallucination Diversity-Aware Active Learning for Text Summarization. arXiv e-prints (2024), arXiv\u20132404."},{"key":"e_1_2_1_223_1","volume-title":"International Conference on Machine Learning. PMLR, 38087\u201338099","author":"\u00a0al Guangxuan Xiao","year":"2023","unstructured":"Guangxuan Xiao et\u00a0al. 2023. Smoothquant: Accurate and efficient post-training quantization for large language models. In International Conference on Machine Learning. PMLR, 38087\u201338099."},{"key":"e_1_2_1_224_1","volume-title":"Cfsum: A coarse-to-fine contribution network for multimodal summarization. arXiv preprint arXiv:2307.02716(2023).","author":"\u00a0al Min Xiao","year":"2023","unstructured":"Min Xiao et\u00a0al. 2023. Cfsum: A coarse-to-fine contribution network for multimodal summarization. arXiv preprint arXiv:2307.02716(2023)."},{"key":"e_1_2_1_225_1","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. 5245\u20135263","author":"\u00a0al Wen Xiao","year":"2022","unstructured":"Wen Xiao et\u00a0al. 2022. PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. 5245\u20135263."},{"key":"e_1_2_1_226_1","doi-asserted-by":"crossref","unstructured":"Wen Xiao and Giuseppe Carenini. 2019. Extractive summarization of long documents by combining global and local context. arXiv preprint arXiv:1909.08089(2019).","DOI":"10.18653\/v1\/D19-1298"},{"key":"e_1_2_1_227_1","volume-title":"Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics. 516\u2013528","author":"Xiao Wen","year":"2020","unstructured":"Wen Xiao and Giuseppe Carenini. 2020. Systematically Exploring Redundancy Reduction in Summarizing Long Documents. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics. 516\u2013528."},{"key":"e_1_2_1_228_1","doi-asserted-by":"crossref","unstructured":"Wen Xiao and Giuseppe Carenini. 2022. Entity-based spancopy for abstractive summarization to improve the factual consistency. arXiv preprint arXiv:2209.03479(2022).","DOI":"10.18653\/v1\/2023.codi-1.9"},{"key":"e_1_2_1_229_1","volume-title":"Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming. 1\u201310","author":"\u00a0al F Xu","year":"2022","unstructured":"Frank\u00a0F Xu et\u00a0al. 2022. A systematic evaluation of large language models of code. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming. 1\u201310."},{"key":"e_1_2_1_230_1","volume-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2826\u20132835","author":"\u00a0al Hongyan Xu","year":"2023","unstructured":"Hongyan Xu et\u00a0al. 2023. Sentiment-aware Review Summarization with Personalized Multi-task Fine-tuning. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2826\u20132835."},{"key":"e_1_2_1_231_1","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 5021\u20135031","author":"\u00a0al Jiacheng Xu","year":"2020","unstructured":"Jiacheng Xu et\u00a0al. 2020. Discourse-Aware Neural Extractive Text Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 5021\u20135031."},{"key":"e_1_2_1_232_1","volume-title":"Identifying Factual Inconsistency in Summaries: Towards Effective Utilization of Large Language Model. arXiv e-prints","author":"\u00a0al Liyan Xu","year":"2024","unstructured":"Liyan Xu et\u00a0al. 2024. Identifying Factual Inconsistency in Summaries: Towards Effective Utilization of Large Language Model. arXiv e-prints (2024), arXiv\u20132402."},{"key":"e_1_2_1_233_1","volume-title":"Proceedings of the AAAI conference on artificial intelligence, Vol.\u00a0 36","author":"\u00a0al Shusheng Xu","year":"2022","unstructured":"Shusheng Xu et\u00a0al. 2022. Sequence level contrastive learning for text summarization. In Proceedings of the AAAI conference on artificial intelligence, Vol.\u00a0 36. 11556\u201311565."},{"key":"e_1_2_1_234_1","unstructured":"Xiaohan Xu et\u00a0al. 2024. A survey on knowledge distillation of large language models. arXiv preprint arXiv:2402.13116(2024)."},{"key":"e_1_2_1_235_1","volume-title":"Findings of the Association for Computational Linguistics: EMNLP","author":"\u00a0al Yichong Xu","year":"2023","unstructured":"Yichong Xu et\u00a0al. 2023. InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT. In Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics, Singapore, 13879\u201313892."},{"key":"e_1_2_1_236_1","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics. 249\u2013255","author":"\u00a0al Shweta Yadav","year":"2021","unstructured":"Shweta Yadav et\u00a0al. 2021. Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics. 249\u2013255."},{"key":"e_1_2_1_237_1","volume-title":"Proceedings of the 27th international conference on computational linguistics. 1110\u20131120","author":"\u00a0al Min Yang","year":"2018","unstructured":"Min Yang et\u00a0al. 2018. Aspect and sentiment aware abstractive review summarization. In Proceedings of the 27th international conference on computational linguistics. 1110\u20131120."},{"key":"e_1_2_1_238_1","volume-title":"Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1\u201320","author":"\u00a0al Saelyne Yang","year":"2022","unstructured":"Saelyne Yang et\u00a0al. 2022. Catchlive: Real-time summarization of live streams with stream content and interaction data. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1\u201320."},{"key":"e_1_2_1_239_1","volume-title":"Proceedings of the 39th International Conference on Computer-Aided Design. 1\u20139.","author":"\u00a0al Xiaoxuan Yang","year":"2020","unstructured":"Xiaoxuan Yang et\u00a0al. 2020. ReTransformer: ReRAM-based processing-in-memory architecture for transformer acceleration. In Proceedings of the 39th International Conference on Computer-Aided Design. 1\u20139."},{"key":"e_1_2_1_240_1","volume-title":"2022 56th Asilomar Conference on Signals, Systems, and Computers. IEEE, 317\u2013321","author":"\u00a0al Xiaoxuan Yang","year":"2022","unstructured":"Xiaoxuan Yang et\u00a0al. 2022. On building efficient and robust neural network designs. In 2022 56th Asilomar Conference on Signals, Systems, and Computers. IEEE, 317\u2013321."},{"key":"e_1_2_1_241_1","volume-title":"Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization. arXiv e-prints","author":"\u00a0al Xianjun Yang","year":"2023","unstructured":"Xianjun Yang et\u00a0al. 2023. Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization. arXiv e-prints (2023), arXiv\u20132302."},{"key":"e_1_2_1_242_1","volume-title":"Proceedings of the AAAI conference on artificial intelligence, Vol.\u00a0 33","author":"\u00a0al Liang Yao","year":"2019","unstructured":"Liang Yao et\u00a0al. 2019. Graph convolutional networks for text classification. In Proceedings of the AAAI conference on artificial intelligence, Vol.\u00a0 33. 7370\u20137377."},{"key":"e_1_2_1_243_1","volume-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2604\u20132620","author":"\u00a0al Zonghai Yao","year":"2023","unstructured":"Zonghai Yao et\u00a0al. 2023. Improving Summarization with Human Edits. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2604\u20132620."},{"key":"e_1_2_1_244_1","volume-title":"Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL","author":"\u00a0al Michihiro Yasunaga","year":"2017","unstructured":"Michihiro Yasunaga et\u00a0al. 2017. Graph-based Neural Multi-Document Summarization. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017). 452\u2013462."},{"key":"e_1_2_1_245_1","unstructured":"Wenpeng Yin and Yulong Pei. 2015. Optimizing sentence modeling and selection for document summarization. In Twenty-fourth international joint conference on artificial intelligence."},{"key":"e_1_2_1_246_1","unstructured":"Wenhao Yu et\u00a0al. 2023. Improving language models via plug-and-play retrieval feedback. arXiv preprint arXiv:2305.14002(2023)."},{"key":"e_1_2_1_247_1","unstructured":"Haohan Yuan and Haopeng Zhang. 2024. DomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text Summarization. arXiv preprint arXiv:2410.15687(2024)."},{"key":"e_1_2_1_248_1","volume-title":"Bartscore: Evaluating generated text as text generation. Advances in Neural Information Processing Systems 34","author":"\u00a0al Weizhe Yuan","year":"2021","unstructured":"Weizhe Yuan et\u00a0al. 2021. Bartscore: Evaluating generated text as text generation. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_2_1_249_1","first-page":"528","article-title":"Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control","volume":"2022","author":"\u00a0al Haopeng Zhang","year":"2022","unstructured":"Haopeng Zhang et\u00a0al. 2022. Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control. In Findings of the Association for Computational Linguistics: NAACL 2022. 528\u2013535.","journal-title":"Findings of the Association for Computational Linguistics: NAACL"},{"key":"e_1_2_1_250_1","first-page":"123","article-title":"Unsupervised Multi-document Summarization with Holistic Inference","volume":"2023","author":"\u00a0al Haopeng Zhang","year":"2023","unstructured":"Haopeng Zhang et\u00a0al. 2023. Unsupervised Multi-document Summarization with Holistic Inference. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023. 123\u2013133.","journal-title":"Findings of the Association for Computational Linguistics: IJCNLP-AACL"},{"key":"e_1_2_1_251_1","volume-title":"Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 10167\u201310176","author":"Zhang Haopeng","year":"2022","unstructured":"Haopeng Zhang, Xiao Liu, and Jiawei Zhang. 2022. HEGEL: Hypergraph Transformer for Long Document Summarization. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 10167\u201310176."},{"key":"e_1_2_1_252_1","volume-title":"Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI","author":"Zhang Haopeng","year":"2023","unstructured":"Haopeng Zhang, Xiao Liu, and Jiawei Zhang. 2023. Contrastive Hierarchical Discourse Graph for Scientific Document Summarization. In Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023). 37\u201347."},{"key":"e_1_2_1_253_1","first-page":"13089","article-title":"DiffuSum: Generation Enhanced Extractive Summarization with Diffusion","volume":"2023","author":"Zhang Haopeng","year":"2023","unstructured":"Haopeng Zhang, Xiao Liu, and Jiawei Zhang. 2023. DiffuSum: Generation Enhanced Extractive Summarization with Diffusion. In Findings of the Association for Computational Linguistics: ACL 2023. 13089\u201313100.","journal-title":"Findings of the Association for Computational Linguistics: ACL"},{"key":"e_1_2_1_254_1","first-page":"3270","article-title":"Extractive Summarization via ChatGPT for Faithful Summary Generation","volume":"2023","author":"Zhang Haopeng","year":"2023","unstructured":"Haopeng Zhang, Xiao Liu, and Jiawei Zhang. 2023. Extractive Summarization via ChatGPT for Faithful Summary Generation. In Findings of the Association for Computational Linguistics: EMNLP 2023. 3270\u20133278.","journal-title":"Findings of the Association for Computational Linguistics: EMNLP"},{"key":"e_1_2_1_255_1","doi-asserted-by":"crossref","unstructured":"Haopeng Zhang Xiao Liu and Jiawei Zhang. 2023. SummIt: Iterative Text Summarization via ChatGPT. arXiv preprint arXiv:2305.14835(2023).","DOI":"10.18653\/v1\/2023.findings-emnlp.714"},{"key":"e_1_2_1_256_1","volume-title":"Conference on Empirical Methods in Natural Language Processing (EMNLP).","author":"Zhang Haopeng","year":"2020","unstructured":"Haopeng Zhang and Jiawei Zhang. 2020. Text graph transformer for document classification. In Conference on Empirical Methods in Natural Language Processing (EMNLP)."},{"key":"e_1_2_1_257_1","unstructured":"Jiawei Zhang et\u00a0al. 2020. Graph-Bert: Only Attention is Needed for Learning Graph Representations. arXiv preprint arXiv:2001.05140(2020)."},{"key":"e_1_2_1_258_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning. 11328\u201311339","author":"\u00a0al Jingqing Zhang","year":"2020","unstructured":"Jingqing Zhang et\u00a0al. 2020. PEGASUS: pre-training with extracted gap-sentences for abstractive summarization. In Proceedings of the 37th International Conference on Machine Learning. 11328\u201311339."},{"key":"e_1_2_1_259_1","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 446\u2013456","author":"Zhang Rui","year":"2019","unstructured":"Rui Zhang and Joel Tetreault. 2019. This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 446\u2013456."},{"key":"e_1_2_1_260_1","volume-title":"BERTScore: Evaluating Text Generation with BERT. In International Conference on Learning Representations.","author":"\u00a0al Tianyi Zhang","year":"2019","unstructured":"Tianyi Zhang et\u00a0al. 2019. BERTScore: Evaluating Text Generation with BERT. In International Conference on Learning Representations."},{"key":"e_1_2_1_261_1","volume-title":"Benchmarking Large Language Models for News Summarization. Transactions of the Association for Computational Linguistics 12","author":"\u00a0al Tianyi Zhang","year":"2024","unstructured":"Tianyi Zhang et\u00a0al. 2024. Benchmarking Large Language Models for News Summarization. Transactions of the Association for Computational Linguistics 12 (2024)."},{"key":"e_1_2_1_262_1","doi-asserted-by":"crossref","unstructured":"Xingxing Zhang et\u00a0al. 2018. Neural latent extractive document summarization. arXiv preprint arXiv:1808.07187(2018).","DOI":"10.18653\/v1\/D18-1088"},{"key":"e_1_2_1_263_1","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 5059\u20135069","author":"\u00a0al Xingxing Zhang","year":"2019","unstructured":"Xingxing Zhang et\u00a0al. 2019. HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 5059\u20135069."},{"key":"e_1_2_1_264_1","unstructured":"Yusen Zhang et\u00a0al. 2023. Fair Abstractive Summarization of Diverse Perspectives. arXiv preprint arXiv:2311.07884(2023)."},{"key":"e_1_2_1_265_1","unstructured":"Yue Zhang et\u00a0al. 2023. Siren\u2019s song in the AI ocean: a survey on hallucination in large language models. arXiv preprint arXiv:2309.01219(2023)."},{"key":"e_1_2_1_266_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 2513\u20132520","author":"Zhao Bin","year":"2014","unstructured":"Bin Zhao and Eric\u00a0P Xing. 2014. Quasi real-time summarization for consumer videos. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2513\u20132520."},{"key":"e_1_2_1_267_1","doi-asserted-by":"crossref","unstructured":"Chenlong Zhao et\u00a0al. 2024. Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level. arXiv preprint arXiv:2405.10202(2024).","DOI":"10.18653\/v1\/2024.findings-naacl.45"},{"key":"e_1_2_1_268_1","doi-asserted-by":"crossref","unstructured":"Wei Zhao et\u00a0al. 2019. MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. arXiv preprint arXiv:1909.02622(2019).","DOI":"10.18653\/v1\/D19-1053"},{"key":"e_1_2_1_269_1","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 6236\u20136247","author":"Zheng Hao","year":"2019","unstructured":"Hao Zheng and Mirella Lapata. 2019. Sentence Centrality Revisited for Unsupervised Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 6236\u20136247."},{"key":"e_1_2_1_270_1","doi-asserted-by":"crossref","unstructured":"Ming Zhong et\u00a0al. 2019. A closer look at data bias in neural extractive summarization models. arXiv preprint arXiv:1909.13705(2019).","DOI":"10.18653\/v1\/D19-5410"},{"key":"e_1_2_1_271_1","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 6197\u20136208","author":"\u00a0al Ming Zhong","year":"2020","unstructured":"Ming Zhong et\u00a0al. 2020. Extractive Summarization as Text Matching. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 6197\u20136208."},{"key":"e_1_2_1_272_1","volume-title":"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics. 5905\u20135921","author":"\u00a0al Ming Zhong","year":"2021","unstructured":"Ming Zhong et\u00a0al. 2021. QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics. 5905\u20135921."},{"key":"e_1_2_1_273_1","volume-title":"Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2023\u20132038","author":"\u00a0al Ming Zhong","year":"2022","unstructured":"Ming Zhong et\u00a0al. 2022. Towards a Unified Multi-Dimensional Evaluator for Text Generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2023\u20132038."},{"key":"e_1_2_1_274_1","doi-asserted-by":"crossref","unstructured":"Ming Zhong et\u00a0al. 2022. Unsupervised multi-granularity summarization. arXiv preprint arXiv:2201.12502(2022).","DOI":"10.18653\/v1\/2022.findings-emnlp.366"},{"key":"e_1_2_1_275_1","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 654\u2013663","author":"\u00a0al Qingyu Zhou","year":"2018","unstructured":"Qingyu Zhou et\u00a0al. 2018. Neural Document Summarization by Jointly Learning to Score and Select Sentences. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 654\u2013663."},{"key":"e_1_2_1_276_1","first-page":"6893","article-title":"Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization","volume":"2023","author":"\u00a0al Weixiao Zhou","year":"2023","unstructured":"Weixiao Zhou et\u00a0al. 2023. Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2023. 6893\u20136908.","journal-title":"Findings of the Association for Computational Linguistics: EMNLP"},{"key":"e_1_2_1_277_1","volume-title":"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics. 718\u2013733","author":"\u00a0al Chenguang Zhu","year":"2021","unstructured":"Chenguang Zhu et\u00a0al. 2021. Enhancing Factual Consistency of Abstractive Summarization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics. 718\u2013733."},{"key":"e_1_2_1_278_1","unstructured":"Chenguang Zhu et\u00a0al. 2021. MediaSum: A large-scale media interview dataset for dialogue summarization. arXiv preprint arXiv:2103.06410(2021)."},{"key":"e_1_2_1_279_1","volume-title":"Proceedings of the 2018 Conference on empirical methods in natural language processing. 4154\u20134164","author":"\u00a0al Junnan Zhu","year":"2018","unstructured":"Junnan Zhu et\u00a0al. 2018. MSMO: Multimodal summarization with multimodal output. In Proceedings of the 2018 Conference on empirical methods in natural language processing. 4154\u20134164."},{"key":"e_1_2_1_280_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence, Vol.\u00a0 34","author":"\u00a0al Junnan Zhu","year":"2020","unstructured":"Junnan Zhu et\u00a0al. 2020. Multimodal summarization with guidance of multimodal reference. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol.\u00a0 34. 9749\u20139756."}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3731445","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,19]],"date-time":"2025-04-19T06:18:19Z","timestamp":1745043499000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3731445"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,19]]},"references-count":279,"alternative-id":["10.1145\/3731445"],"URL":"https:\/\/doi.org\/10.1145\/3731445","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,19]]},"assertion":[{"value":"2024-06-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"3731445"}}