{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T16:15:44Z","timestamp":1771258544998,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T00:00:00Z","timestamp":1679270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T00:00:00Z","timestamp":1679270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012456","name":"National Social Science Fund of China","doi-asserted-by":"publisher","award":["19BYY076"],"award-info":[{"award-number":["19BYY076"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021MF064"],"award-info":[{"award-number":["ZR2021MF064"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s11227-023-05165-8","type":"journal-article","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T01:58:54Z","timestamp":1679882334000},"page":"12949-12977","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["SeburSum: a novel set-based summary ranking strategy for summary-level extractive summarization"],"prefix":"10.1007","volume":"79","author":[{"given":"Shuai","family":"Gong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenfang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangtao","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenqing","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunling","family":"Tong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,20]]},"reference":[{"key":"5165_CR1","doi-asserted-by":"publisher","unstructured":"Liu Y & Lapata M (2019) Text summarization with pretrained encoders. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp 3730\u20133740). Association for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-1387. https:\/\/aclanthology.org\/D19-1387","DOI":"10.18653\/v1\/D19-1387"},{"key":"5165_CR2","doi-asserted-by":"publisher","unstructured":"Wang D, Liu P, Zheng Y, Qiu X, Huang, X (2020) Heterogeneous graph neural networks for extractive document summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 6209\u20136219. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.553. https:\/\/aclanthology.org\/2020.acl-main.553","DOI":"10.18653\/v1\/2020.acl-main.553"},{"key":"5165_CR3","doi-asserted-by":"publisher","unstructured":"Jia R, Cao Y, Fang F, Zhou Y, Fang Z, Liu Y, Wang S (2021) Deep differential amplifier for extractive summarization. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp 366\u2013376. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.31. https:\/\/aclanthology.org\/2021.acl-long.31","DOI":"10.18653\/v1\/2021.acl-long.31"},{"key":"5165_CR4","doi-asserted-by":"publisher","unstructured":"Ruan Q, Ostendorff M, Rehm G (2022) HiStruct+: Improving extractive text summarization with hierarchical structure information. In: Findings of the Association for Computational Linguistics: ACL 2022, pp 1292\u20131308. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.findings-acl.102. https:\/\/aclanthology.org\/2022.findings-acl.102","DOI":"10.18653\/v1\/2022.findings-acl.102"},{"key":"5165_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109460","volume":"252","author":"Q Xie","year":"2022","unstructured":"Xie Q, Bishop JA, Tiwari P, Ananiadou S (2022) Pre-trained language models with domain knowledge for biomedical extractive summarization. Knowl-Based Syst 252:109460","journal-title":"Knowl-Based Syst"},{"key":"5165_CR6","doi-asserted-by":"publisher","unstructured":"Zhong M, Liu P, Chen Y, Wang D, Qiu X, Huang X (2020) Extractive summarization as text matching. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 6197\u20136208. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.552. https:\/\/aclanthology.org\/2020.acl-main.552","DOI":"10.18653\/v1\/2020.acl-main.552"},{"key":"5165_CR7","doi-asserted-by":"crossref","unstructured":"Lin C.-Y, Hovy E (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, pp 150\u2013157. https:\/\/aclanthology.org\/N03-1020","DOI":"10.3115\/1073445.1073465"},{"key":"5165_CR8","unstructured":"Zhuang L, Wayne L, Ya S, Jun Z (2021) A robustly optimized BERT pre-training approach with post-training. In: Proceedings of the 20th Chinese National Conference on Computational Linguistics, pp 1218\u20131227. Chinese Information Processing Society of China, Huhhot, China. https:\/\/aclanthology.org\/2021.ccl-1.108"},{"key":"5165_CR9","doi-asserted-by":"publisher","unstructured":"Liu Y, Liu P (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 (Volume 2: Short Papers), pp 1065\u20131072. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.acl-short.135. https:\/\/aclanthology.org\/2021.acl-short.135","DOI":"10.18653\/v1\/2021.acl-short.135"},{"key":"5165_CR10","doi-asserted-by":"crossref","unstructured":"Gu N, Ash E, Hahnloser R (2022) Memsum: Extractive summarization of long documents using multi-step episodic markov decision processes. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 6507\u20136522","DOI":"10.18653\/v1\/2022.acl-long.450"},{"key":"5165_CR11","unstructured":"Hermann KM, Kocisk\u00fd T, Grefenstette E, Espeholt L, Kay W, Suleyman M, Blunsom P (2015) Teaching machines to read and comprehend. In: Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R (eds) Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pp 1693\u20131701. https:\/\/proceedings.neurips.cc\/paper\/2015\/hash\/afdec7005cc9f14302cd0474fd0f3c96-Abstract.html"},{"key":"5165_CR12","doi-asserted-by":"publisher","unstructured":"Narayan S, Cohen SB, Lapata M (2018) Don\u2019t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp 1797\u20131807. Association for Computational Linguistics, Brussels, Belgium. https:\/\/doi.org\/10.18653\/v1\/D18-1206. https:\/\/aclanthology.org\/D18-1206","DOI":"10.18653\/v1\/D18-1206"},{"key":"5165_CR13","doi-asserted-by":"publisher","unstructured":"Kim B, Kim H, Kim G (2019) Abstractive summarization of Reddit posts with multi-level memory networks. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp 2519\u20132531. Association for Computational Linguistics, Minneapolis, Minnesota. https:\/\/doi.org\/10.18653\/v1\/N19-1260. https:\/\/aclanthology.org\/N19-1260","DOI":"10.18653\/v1\/N19-1260"},{"key":"5165_CR14","doi-asserted-by":"crossref","unstructured":"Gao T, Yao X, Chen D (2021) Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"5165_CR15","doi-asserted-by":"crossref","unstructured":"Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), vol. 2, pp 1735\u20131742. IEEE","DOI":"10.1109\/CVPR.2006.100"},{"key":"5165_CR16","doi-asserted-by":"crossref","unstructured":"Mai S, Zeng Y, Zheng S, Hu H (2022) Hybrid contrastive learning of tri-modal representation for multimodal sentiment analysis. IEEE Trans Affect Comput","DOI":"10.1109\/TAFFC.2022.3172360"},{"issue":"1","key":"5165_CR17","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/s10462-022-10183-8","volume":"56","author":"JY-L Chan","year":"2023","unstructured":"Chan JY-L, Bea KT, Leow SMH, Phoong SW, Cheng WK (2023) State of the art: a review of sentiment analysis based on sequential transfer learning. Artif Intell Rev 56(1):749\u2013780","journal-title":"Artif Intell Rev"},{"key":"5165_CR18","doi-asserted-by":"crossref","unstructured":"Caciularu A, Dagan I, Goldberger J, Cohan A (2022) Long context question answering via supervised contrastive learning. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 2872\u20132879","DOI":"10.18653\/v1\/2022.naacl-main.207"},{"key":"5165_CR19","doi-asserted-by":"crossref","first-page":"76","DOI":"10.18653\/v1\/2022.findings-emnlp.6","volume":"2022","author":"L Zhang","year":"2022","unstructured":"Zhang L, Li R (2022) Ke-gcl: Knowledge enhanced graph contrastive learning for commonsense question answering. Find Assoc Comput Linguist EMNLP 2022:76\u201387","journal-title":"Find Assoc Comput Linguist EMNLP"},{"key":"5165_CR20","doi-asserted-by":"crossref","unstructured":"Cao S, Wang L (2021) Cliff: Contrastive learning for improving faithfulness and factuality in abstractive summarization. arXiv preprint arXiv:2109.09209","DOI":"10.18653\/v1\/2021.emnlp-main.532"},{"key":"5165_CR21","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (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: Human Language Technologies, Volume 1 (Long and Short Papers), pp 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota. https:\/\/doi.org\/10.18653\/v1\/N19-1423. https:\/\/aclanthology.org\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"5165_CR22","doi-asserted-by":"crossref","unstructured":"Nallapati R, Zhai F, Zhou B (2017) Summarunner: A recurrent neural network based sequence model for extractive summarization of documents. In: Singh SP, Markovitch S. (eds.) Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, pp 3075\u20133081. AAAI Press, Palo Alto. http:\/\/aaai.org\/ocs\/index.php\/AAAI\/AAAI17\/paper\/view\/14636","DOI":"10.1609\/aaai.v31i1.10958"},{"key":"5165_CR23","doi-asserted-by":"crossref","unstructured":"Zhou Q, Yang N, Wei F, Huang S, Zhou M, Zhao T (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 (Volume 1: Long Papers), pp 654\u2013663","DOI":"10.18653\/v1\/P18-1061"},{"key":"5165_CR24","doi-asserted-by":"publisher","unstructured":"Zhang X, Wei F, Zhou M (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, pp 5059\u20135069. Association for Computational Linguistics, Florence, Italy. https:\/\/doi.org\/10.18653\/v1\/P19-1499. https:\/\/aclanthology.org\/P19-1499","DOI":"10.18653\/v1\/P19-1499"},{"key":"5165_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118442","volume":"211","author":"A Joshi","year":"2023","unstructured":"Joshi A, Fidalgo E, Alegre E, Fern\u00e1ndez-Robles L (2023) Deepsumm: exploiting topic models and sequence to sequence networks for extractive text summarization. Expert Syst Appl 211:118442","journal-title":"Expert Syst Appl"},{"key":"5165_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119308","volume":"215","author":"A Ghadimi","year":"2023","unstructured":"Ghadimi A, Beigy H (2023) Sgcsumm: An extractive multi-document summarization method based on pre-trained language model, submodularity, and graph convolutional neural networks. Expert Syst Appl 215:119308","journal-title":"Expert Syst Appl"},{"key":"5165_CR27","doi-asserted-by":"publisher","unstructured":"Jia R, Cao Y, Tang H, Fang F, Cao C, Wang S (2020) Neural extractive summarization with hierarchical attentive heterogeneous graph network. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 3622\u20133631. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.295. https:\/\/aclanthology.org\/2020.emnlp-main.295","DOI":"10.18653\/v1\/2020.emnlp-main.295"},{"key":"5165_CR28","doi-asserted-by":"publisher","unstructured":"Jadhav A, Rajan V (2018) Extractive summarization with SWAP-NET: Sentences and words from alternating pointer networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 142\u2013151. Association for Computational Linguistics, Melbourne, Australia. https:\/\/doi.org\/10.18653\/v1\/P18-1014. https:\/\/aclanthology.org\/P18-1014","DOI":"10.18653\/v1\/P18-1014"},{"key":"5165_CR29","doi-asserted-by":"publisher","unstructured":"Narayan S, Cohen SB, Lapata M (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: Human Language Technologies, Volume 1 (Long Papers), pp 1747\u20131759. Association for Computational Linguistics, New Orleans, Louisiana. https:\/\/doi.org\/10.18653\/v1\/N18-1158. https:\/\/aclanthology.org\/N18-1158","DOI":"10.18653\/v1\/N18-1158"},{"key":"5165_CR30","doi-asserted-by":"publisher","unstructured":"Arumae K, Liu F (2018) Reinforced extractive summarization with question-focused rewards. In: Proceedings of ACL 2018, Student Research Workshop, pp 105\u2013111. Association for Computational Linguistics, Melbourne, Australia. https:\/\/doi.org\/10.18653\/v1\/P18-3015. https:\/\/aclanthology.org\/P18-3015","DOI":"10.18653\/v1\/P18-3015"},{"key":"5165_CR31","doi-asserted-by":"publisher","unstructured":"Luo L, Ao X, Song Y, Pan F, Yang M, He Q (2019) Reading like HER: Human reading inspired extractive summarization. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 3033\u20133043. Association for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-1300. https:\/\/aclanthology.org\/D19-1300","DOI":"10.18653\/v1\/D19-1300"},{"key":"5165_CR32","doi-asserted-by":"publisher","unstructured":"Gu N, Ash E, Hahnloser R (2022) MemSum: Extractive summarization of long documents using multi-step episodic Markov decision processes. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 6507\u20136522. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.450. https:\/\/aclanthology.org\/2022.acl-long.450","DOI":"10.18653\/v1\/2022.acl-long.450"},{"key":"5165_CR33","doi-asserted-by":"publisher","unstructured":"Zheng H, Lapata M (2019) Sentence centrality revisited for unsupervised summarization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 6236\u20136247. Association for Computational Linguistics, Florence, Italy. https:\/\/doi.org\/10.18653\/v1\/P19-1628. https:\/\/aclanthology.org\/P19-1628","DOI":"10.18653\/v1\/P19-1628"},{"key":"5165_CR34","unstructured":"Mihalcea R, Tarau P (2004) TextRank: Bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp 404\u2013411. Association for Computational Linguistics, Barcelona, Spain. https:\/\/aclanthology.org\/W04-3252"},{"key":"5165_CR35","doi-asserted-by":"crossref","unstructured":"Gudakahriz, S.J, Moghadam, A.M.E, Mahmoudi, F (2022) Opinion texts summarization based on texts concepts with multi-objective pruning approach. J Supercomput, pp 1\u201324","DOI":"10.1007\/s11227-022-04842-4"},{"issue":"1\u20137","key":"5165_CR36","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/S0169-7552(98)00110-X","volume":"30","author":"S Brin","year":"1998","unstructured":"Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1\u20137):107\u2013117","journal-title":"Comput Netw ISDN Syst"},{"key":"5165_CR37","doi-asserted-by":"publisher","unstructured":"Xu S, Zhang X, Wu Y, Wei F, Zhou M (2020) Unsupervised extractive summarization by pre-training hierarchical transformers. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1784\u20131795. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.161. https:\/\/aclanthology.org\/2020.findings-emnlp.161","DOI":"10.18653\/v1\/2020.findings-emnlp.161"},{"key":"5165_CR38","doi-asserted-by":"publisher","unstructured":"Liang X, Wu S, Li M, Li Z (2021) Improving unsupervised extractive summarization with facet-aware modeling. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp 1685\u20131697. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.147. https:\/\/aclanthology.org\/2021.findings-acl.147","DOI":"10.18653\/v1\/2021.findings-acl.147"},{"key":"5165_CR39","unstructured":"Paulus R, Xiong C, Socher R (2017) A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304"},{"key":"5165_CR40","unstructured":"Wan, X, Cao, Z, Wei, F, Li, S, Zhou, M (2015) Multi-document summarization via discriminative summary reranking. arXiv preprint arXiv:1507.02062"},{"key":"5165_CR41","doi-asserted-by":"publisher","unstructured":"Zhang D, Nan F, Wei X, Li S-W, Zhu H, McKeown K, Nallapati R, Arnold AO, Xiang B (2021) Supporting clustering with contrastive learning. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 5419\u20135430. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.427. https:\/\/aclanthology.org\/2021.naacl-main.427","DOI":"10.18653\/v1\/2021.naacl-main.427"},{"key":"5165_CR42","unstructured":"Gunel B, Du J, Conneau A, Stoyanov V (2020) Supervised contrastive learning for pre-trained language model fine-tuning. arXiv preprint arXiv:2011.01403"},{"key":"5165_CR43","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.1609\/aaai.v33i01.33016999","volume":"33","author":"J Shi","year":"2019","unstructured":"Shi J, Liang C, Hou L, Li J, Liu Z, Zhang H (2019) Deepchannel: Salience estimation by contrastive learning for extractive document summarization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp 6999\u20137006","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"5165_CR44","doi-asserted-by":"crossref","unstructured":"Wu H, Ma T, Wu L, Manyumwa T, Ji S (2020) Unsupervised reference-free summary quality evaluation via contrastive learning. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 3612\u20133621","DOI":"10.18653\/v1\/2020.emnlp-main.294"},{"key":"5165_CR45","doi-asserted-by":"publisher","first-page":"11556","DOI":"10.1609\/aaai.v36i10.21409","volume":"36","author":"S Xu","year":"2022","unstructured":"Xu S, Zhang X, Wu Y, Wei F (2022) Sequence level contrastive learning for text summarization. In: Proceedings of the AAAI Conference on Artificial Intelligence vol 36, pp 11556\u201311565","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"5165_CR46","unstructured":"An, C, Zhong, M, Wu, Z, Zhu, Q, Huang, X.-J, Qiu, X (2022) Colo: A contrastive learning based re-ranking framework for one-stage summarization. In: Proceedings of the 29th International Conference on Computational Linguistics, pp 5783\u20135793"},{"key":"5165_CR47","doi-asserted-by":"crossref","unstructured":"Wang F, Liu H (2021) Understanding the behaviour of contrastive loss. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2495\u20132504","DOI":"10.1109\/CVPR46437.2021.00252"},{"key":"5165_CR48","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"key":"5165_CR49","doi-asserted-by":"publisher","unstructured":"Manning C, Surdeanu M, Bauer J, Finkel J, Bethard S, McClosky D (2014) The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 55\u201360. Association for Computational Linguistics, Baltimore, Maryland. https:\/\/doi.org\/10.3115\/v1\/P14-5010. https:\/\/aclanthology.org\/P14-5010","DOI":"10.3115\/v1\/P14-5010"},{"key":"5165_CR50","doi-asserted-by":"publisher","unstructured":"See A, Liu PJ, Manning CD (2017) Get to the point: Summarization with pointer-generator networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 1073\u20131083. Association for Computational Linguistics, Vancouver, Canada. https:\/\/doi.org\/10.18653\/v1\/P17-1099. https:\/\/aclanthology.org\/P17-1099","DOI":"10.18653\/v1\/P17-1099"},{"key":"5165_CR51","doi-asserted-by":"publisher","unstructured":"Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, Cistac P, Rault T, Louf R, Funtowicz M, Davison J, Shleifer S, von Platen P, Ma C, Jernite Y, Plu J, Xu C, Le\u00a0Scao T, Gugger S, Drame M, Lhoest Q, Rush A(2020) Transformers: State-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp 38\u201345. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-demos.6. https:\/\/aclanthology.org\/2020.emnlp-demos.6","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"5165_CR52","unstructured":"Vinyals O, Fortunato M, Jaitly N (2015) Pointer networks. In: Cortes, C, Lawrence, N, Lee, D, Sugiyama, M, Garnett, R. (eds.) Advances in neural information processing systems, vol. 28. Curran Associates, Inc, New York. https:\/\/proceedings.neurips.cc\/paper\/2015\/file\/29921001f2f04bd3baee84a12e98098f-Paper.pdf"},{"key":"5165_CR53","doi-asserted-by":"publisher","unstructured":"Narayan S, Maynez J, Adamek J, Pighin D, Bratanic B, McDonald R (2020) Stepwise extractive summarization and planning with structured transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 4143\u20134159. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.339. https:\/\/aclanthology.org\/2020.emnlp-main.339","DOI":"10.18653\/v1\/2020.emnlp-main.339"},{"key":"5165_CR54","doi-asserted-by":"publisher","unstructured":"Ainslie J, Ontanon S, Alberti C, Cvicek V, Fisher Z, Pham P, Ravula A, Sanghai S, Wang Q, Yang L (2020) ETC: Encoding long and structured inputs in transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 268\u2013284. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.19. https:\/\/aclanthology.org\/2020.emnlp-main.19","DOI":"10.18653\/v1\/2020.emnlp-main.19"},{"key":"5165_CR55","doi-asserted-by":"crossref","unstructured":"Bi K, Jha R, Croft B, Celikyilmaz A (2021) AREDSUM: Adaptive redundancy-aware iterative sentence ranking for extractive document summarization. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 281\u2013291. Association for Computational Linguistics. https:\/\/aclanthology.org\/2021.eacl-main.22","DOI":"10.18653\/v1\/2021.eacl-main.22"},{"key":"5165_CR56","doi-asserted-by":"publisher","first-page":"13134","DOI":"10.1609\/aaai.v35i14.17552","volume":"35","author":"R Jia","year":"2021","unstructured":"Jia R, Cao Y, Shi H, Fang F, Yin P, Wang S (2021) Flexible non-autoregressive extractive summarization with threshold: How to extract a non-fixed number of summary sentences. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp 13134\u201313142","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"5165_CR57","unstructured":"Zhang J, Zhao Y, Saleh M, Liu P.J (2019) (1912) PEGASUS: pre-training with extracted gap-sentences for abstractive summarization. CoRR arXiv:abs\/1912.08777"},{"key":"5165_CR58","doi-asserted-by":"publisher","unstructured":"Liu Y, Jia Q, Zhu K (2022) Length control in abstractive summarization by pretraining information selection. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 6885\u20136895. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.474. https:\/\/aclanthology.org\/2022.acl-long.474","DOI":"10.18653\/v1\/2022.acl-long.474"},{"key":"5165_CR59","doi-asserted-by":"publisher","unstructured":"Zhang S, Zhang X, Bao H, Wei F (2022) Attention temperature matters in abstractive summarization distillation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 127\u2013141. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.11. https:\/\/aclanthology.org\/2022.acl-long.11","DOI":"10.18653\/v1\/2022.acl-long.11"},{"key":"5165_CR60","doi-asserted-by":"publisher","unstructured":"Liu Y, Liu P, Radev D, Neubig G (2022) BRIO: Bringing order to abstractive summarization. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 2890\u20132903. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.207. https:\/\/aclanthology.org\/2022.acl-long.207","DOI":"10.18653\/v1\/2022.acl-long.207"},{"key":"5165_CR61","doi-asserted-by":"crossref","unstructured":"Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L (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 pp 7871\u20137880","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"5165_CR62","doi-asserted-by":"publisher","unstructured":"Xing L, Xiao W & Carenini G (2021) Demoting the lead bias in news summarization via alternating adversarial learning. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Vol.2: Short Papers, pp 948\u2013954. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2021.acl-short.119. https:\/\/aclanthology.org\/2021.acl-short.119","DOI":"10.18653\/v1\/2021.acl-short.119"},{"key":"5165_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107235","volume":"227","author":"HP Chan","year":"2021","unstructured":"Chan HP, King I (2021) A condense-then-select strategy for text summarization. Knowl-Based Syst 227:107235","journal-title":"Knowl-Based Syst"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05165-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05165-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05165-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T02:01:29Z","timestamp":1729130489000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05165-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,20]]},"references-count":63,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["5165"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05165-8","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,20]]},"assertion":[{"value":"4 March 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This term is not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}