{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T02:45:53Z","timestamp":1792464353907,"version":"4.5.0"},"reference-count":159,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T00:00:00Z","timestamp":1609632000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100006435","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1619028, IIS-1707498, IIS-1838730"],"award-info":[{"award-number":["IIS-1619028, IIS-1707498, IIS-1838730"]}],"id":[{"id":"10.13039\/100006435","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM\/IMS Trans. Data Sci."],"published-print":{"date-parts":[[2021,2,28]]},"abstract":"<jats:p>In the past few years, neural abstractive text summarization with sequence-to-sequence (seq2seq) models have gained a lot of popularity. Many interesting techniques have been proposed to improve seq2seq models, making them capable of handling different challenges, such as saliency, fluency and human readability, and generate high-quality summaries. Generally speaking, most of these techniques differ in one of these three categories: network structure, parameter inference, and decoding\/generation. There are also other concerns, such as efficiency and parallelism for training a model. In this article, we provide a comprehensive literature survey on different seq2seq models for abstractive text summarization from the viewpoint of network structures, training strategies, and summary generation algorithms. Several models were first proposed for language modeling and generation tasks, such as machine translation, and later applied to abstractive text summarization. Hence, we also provide a brief review of these models. As part of this survey, we also develop an open source library, namely, Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization. An extensive set of experiments have been conducted on the widely used CNN\/Daily Mail dataset to examine the effectiveness of several different neural network components. Finally, we benchmark two models implemented in NATS on the two recently released datasets, namely, Newsroom and Bytecup.<\/jats:p>","DOI":"10.1145\/3419106","type":"journal-article","created":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T12:06:42Z","timestamp":1609675602000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":169,"title":["Neural Abstractive Text Summarization with Sequence-to-Sequence Models"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7604-5665","authenticated-orcid":false,"given":"Tian","family":"Shi","sequence":"first","affiliation":[{"name":"Virginia Tech, Arlington, VA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaser","family":"Keneshloo","sequence":"additional","affiliation":[{"name":"Virginia Tech, Arlington, VA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naren","family":"Ramakrishnan","sequence":"additional","affiliation":[{"name":"Virginia Tech, Arlington, VA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chandan K.","family":"Reddy","sequence":"additional","affiliation":[{"name":"Virginia Tech, Arlington, VA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,1,3]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Text summarization techniques: A brief survey. arXiv preprint arXiv:1707.02268","author":"Allahyari Mehdi","year":"2017","unstructured":"Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, and Krys Kochut. 2017. Text summarization techniques: A brief survey. arXiv preprint arXiv:1707.02268 (2017)."},{"key":"e_1_2_2_2_1","volume-title":"An actor-critic algorithm for sequence prediction. arXiv preprint arXiv:1607.07086","author":"Bahdanau Dzmitry","year":"2016","unstructured":"Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron Courville, and Yoshua Bengio. 2016. An actor-critic algorithm for sequence prediction. arXiv preprint arXiv:1607.07086 (2016)."},{"key":"e_1_2_2_3_1","volume-title":"Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473","author":"Bahdanau Dzmitry","year":"2014","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)."},{"key":"e_1_2_2_4_1","volume-title":"Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 4945--4949","author":"Bahdanau Dzmitry","year":"2016","unstructured":"Dzmitry Bahdanau, Jan Chorowski, Dmitriy Serdyuk, Philemon Brakel, and Yoshua Bengio. 2016. End-to-end attention-based large vocabulary speech recognition. In Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 4945--4949."},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.1986.1169179"},{"key":"e_1_2_2_6_1","unstructured":"Hareesh Bahuleyan Lili Mou Olga Vechtomova and Pascal Poupart. 2018. Variational Attention for Sequence-to-Sequence Models. In COLING."},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045527"},{"key":"e_1_2_2_8_1","unstructured":"Samy Bengio Oriol Vinyals Navdeep Jaitly and Noam Shazeer. 2015. Scheduled sampling for sequence prediction with recurrent neural networks. In Advances in Neural Information Processing Systems. 1171--1179."},{"key":"e_1_2_2_9_1","first-page":"1137","article-title":"A neural probabilistic language model","author":"Bengio Yoshua","year":"2003","unstructured":"Yoshua Bengio, R\u00e9jean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A neural probabilistic language model. Journal of Machine Learning Research 3, Feb (2003), 1137--1155.","journal-title":"Journal of Machine Learning Research 3"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"e_1_2_2_11_1","first-page":"39","article-title":"A maximum entropy approach to natural language processing","volume":"22","author":"Adam","year":"1996","unstructured":"Adam L, Berger, Vincent J, Della Pietra, and Stephen A, Della Pietra. 1996. A maximum entropy approach to natural language processing. Computational Linguistics 22, 1 (1996), 39--71.","journal-title":"Computational Linguistics"},{"key":"e_1_2_2_12_1","volume-title":"Proceedings of the 2016 6th International Conference on Cloud System and Big Data Engineering (Confluence). IEEE, 65--72","author":"Bhatia Neelima","year":"2016","unstructured":"Neelima Bhatia and Arunima Jaiswal. 2016. Automatic text summarization and it\u2019s methods-a review. In Proceedings of the 2016 6th International Conference on Cloud System and Big Data Engineering (Confluence). IEEE, 65--72."},{"key":"e_1_2_2_13_1","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 632--642","author":"Bowman Samuel","unstructured":"Samuel Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 632--642."},{"key":"e_1_2_2_14_1","volume-title":"Quasi-recurrent neural networks. arXiv preprint arXiv:1611.01576","author":"Bradbury James","year":"2016","unstructured":"James Bradbury, Stephen Merity, Caiming Xiong, and Richard Socher. 2016. Quasi-recurrent neural networks. arXiv preprint arXiv:1611.01576 (2016)."},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1150"},{"key":"e_1_2_2_16_1","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","volume":"1","author":"Chen Danqi","unstructured":"Danqi Chen, Jason Bolton, and Christopher D. Manning. 2016. A thorough examination of the CNN\/daily mail reading comprehension task. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 2358--2367."},{"key":"e_1_2_2_17_1","volume-title":"Proceedings of the 25th International Joint Conference on Artificial Intelligence. AAAI Press, 2754--2760","author":"Chen Qian","year":"2016","unstructured":"Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, and Hui Jiang. 2016. Distraction-based neural networks for modeling documents. In Proceedings of the 25th International Joint Conference on Artificial Intelligence. AAAI Press, 2754--2760."},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1442"},{"key":"e_1_2_2_19_1","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 675--686","author":"Chen Yen-Chun","year":"2018","unstructured":"Yen-Chun Chen and Mohit Bansal. 2018. Fast abstractive summarization with reinforce-selected sentence rewriting. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 675--686."},{"key":"e_1_2_2_20_1","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","volume":"1","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 (Volume 1: Long Papers), Vol. 1. 484--494."},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_2_2_22_1","volume-title":"Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 93--98","author":"Chopra Sumit","unstructured":"Sumit Chopra, Michael Auli, and Alexander M. Rush. 2016. Abstractive sentence summarization with attentive recurrent neural networks. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 93--98."},{"key":"e_1_2_2_23_1","volume-title":"NIPS 2014 Workshop on Deep Learning","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014."},{"key":"e_1_2_2_24_1","unstructured":"Junyoung Chung Kyle Kastner Laurent Dinh Kratarth Goel Aaron C. Courville and Yoshua Bengio. 2015. A recurrent latent variable model for sequential data. In Advances in Neural Information Processing Systems. 2980--2988."},{"key":"e_1_2_2_25_1","volume-title":"Proceedings of the International Conference on Database Systems for Advanced Applications. Springer, 924--932","author":"Chung Tong Lee","year":"2018","unstructured":"Tong Lee Chung, Bin Xu, Yongbin Liu, and Chunping Ouyang. 2018. Main point generator: Summarizing with a focus. In Proceedings of the International Conference on Database Systems for Advanced Applications. Springer, 924--932."},{"key":"e_1_2_2_26_1","volume-title":"Diverse beam search for increased novelty in abstractive summarization. arXiv preprint arXiv:1802.01457","author":"Cibils Andr\u00e9","year":"2018","unstructured":"Andr\u00e9 Cibils, Claudiu Musat, Andreea Hossman, and Michael Baeriswyl. 2018. Diverse beam search for increased novelty in abstractive summarization. arXiv preprint arXiv:1802.01457 (2018)."},{"key":"e_1_2_2_27_1","volume-title":"International Conference on Learning Representations.","author":"Clark Kevin","unstructured":"Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2019. ELECTRA: Pre-training text encoders as discriminators rather than generators. In International Conference on Learning Representations."},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2097"},{"key":"e_1_2_2_29_1","volume-title":"Proceedings of the 2013 6th International Conference on Emerging Trends in Engineering and Technology (ICETET). IEEE, 109--110","author":"Dalal Vipul","unstructured":"Vipul Dalal and Latesh G. Malik. 2013. A survey of extractive and abstractive text summarization techniques. In Proceedings of the 2013 6th International Conference on Emerging Trends in Engineering and Technology (ICETET). IEEE, 109--110."},{"key":"e_1_2_2_30_1","volume-title":"Martins","author":"Das Dipanjan","year":"2007","unstructured":"Dipanjan Das and Andr\u00e9 F. T. Martins. 2007. A survey on automatic text summarization. Literature Survey for the Language and Statistics II course at CMU 4 (2007), 192--195."},{"key":"e_1_2_2_31_1","volume-title":"Proceedings of the International Conference on Machine Learning. 933--941","author":"Yann","year":"2017","unstructured":"Yann N, Dauphin, Angela Fan, Michael Auli, and David Grangier. 2017. Language modeling with gated convolutional networks. In Proceedings of the International Conference on Machine Learning. 933--941."},{"key":"e_1_2_2_32_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 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). 4171--4186."},{"key":"e_1_2_2_33_1","volume-title":"Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908","author":"Doersch Carl","year":"2016","unstructured":"Carl Doersch. 2016. Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908 (2016)."},{"key":"e_1_2_2_34_1","unstructured":"Li Dong Nan Yang Wenhui Wang Furu Wei Xiaodong Liu Yu Wang Jianfeng Gao Ming Zhou and Hsiao-Wuen Hon. 2019. Unified language model pre-training for natural language understanding and generation. In Advances in Neural Information Processing Systems. 13042--13054."},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog1402_1"},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1102"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-2706"},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-016-9475-9"},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1012"},{"key":"e_1_2_2_40_1","volume-title":"Proceedings of the International Conference on Machine Learning. 1243--1252","author":"Gehring Jonas","unstructured":"Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017. Convolutional sequence to sequence learning. In Proceedings of the International Conference on Machine Learning. 1243--1252."},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1443"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D13-1111"},{"key":"e_1_2_2_43_1","volume-title":"Proceedings of the International Conference on Machine Learning. 1764--1772","author":"Graves Alex","year":"2014","unstructured":"Alex Graves and Navdeep Jaitly. 2014. Towards end-to-end speech recognition with recurrent neural networks. In Proceedings of the International Conference on Machine Learning. 1764--1772."},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1065"},{"key":"e_1_2_2_45_1","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","volume":"1","author":"Gu Jiatao","unstructured":"Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O. K. Li. 2016. Incorporating copying mechanism in sequence-to-sequence learning. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 1631--1640."},{"key":"e_1_2_2_46_1","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","volume":"1","author":"Gulcehre Caglar","year":"2016","unstructured":"Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. 2016. Pointing the unknown words. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 140--149."},{"key":"e_1_2_2_47_1","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 687--697","author":"Guo Han","year":"2018","unstructured":"Han Guo, Ramakanth Pasunuru, and Mohit Bansal. 2018. Soft layer-specific multi-task summarization with entailment and question generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 687--697."},{"key":"e_1_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835449.1835639"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/511446.511513"},{"key":"e_1_2_2_50_1","unstructured":"Karl Moritz Hermann Tomas Kocisky Edward Grefenstette Lasse Espeholt Will Kay Mustafa Suleyman and Phil Blunsom. 2015. Teaching machines to read and comprehend. In Advances in Neural Information Processing Systems. 1693--1701."},{"key":"e_1_2_2_51_1","volume-title":"Deep Learning","author":"Goodfellow Ian","unstructured":"Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. Volume 1, Number 2."},{"key":"e_1_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_2_53_1","volume-title":"The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751","author":"Holtzman Ari","year":"2019","unstructured":"Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019. The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751 (2019)."},{"key":"e_1_2_2_54_1","volume-title":"A unified model for extractive and abstractive summarization using inconsistency loss. arXiv preprint arXiv:1805.06266","author":"Hsu Wan-Ting","year":"2018","unstructured":"Wan-Ting Hsu, Chieh-Kai Lin, Ming-Ying Lee, Kerui Min, Jing Tang, and Min Sun. 2018. A unified model for extractive and abstractive summarization using inconsistency loss. arXiv preprint arXiv:1805.06266 (2018)."},{"key":"e_1_2_2_55_1","volume-title":"An introductory survey on attention mechanisms in NLP problems. arXiv preprint arXiv:1811.05544","author":"Dichao Hu.","year":"2018","unstructured":"Dichao Hu. 2018. An introductory survey on attention mechanisms in NLP problems. arXiv preprint arXiv:1811.05544 (2018)."},{"key":"e_1_2_2_56_1","volume-title":"Knowledge graph-augmented abstractive summarization with semantic-driven cloze reward. arXiv preprint arXiv:2005.01159","author":"Huang Luyang","year":"2020","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)."},{"key":"e_1_2_2_57_1","volume-title":"Tying word vectors and word classifiers: A loss framework for language modeling. arXiv preprint arXiv:1611.01462","author":"Inan Hakan","year":"2016","unstructured":"Hakan Inan, Khashayar Khosravi, and Richard Socher. 2016. Tying word vectors and word classifiers: A loss framework for language modeling. arXiv preprint arXiv:1611.01462 (2016)."},{"key":"e_1_2_2_58_1","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 3752--3762","author":"Ippolito Daphne","year":"2019","unstructured":"Daphne Ippolito, Reno Kriz, Joao Sedoc, Maria Kustikova, and Chris Callison-Burch. 2019. Comparison of diverse decoding methods from conditional language models. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 3752--3762."},{"key":"e_1_2_2_59_1","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 4067--4077","author":"Jiang Yichen","year":"2018","unstructured":"Yichen Jiang and Mohit Bansal. 2018. Closed-book training to improve summarization encoder memory. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 4067--4077."},{"key":"e_1_2_2_60_1","volume-title":"Neural machine translation in linear time. arXiv preprint arXiv:1610.10099","author":"Kalchbrenner Nal","year":"2016","unstructured":"Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, and Koray Kavukcuoglu. 2016. Neural machine translation in linear time. arXiv preprint arXiv:1610.10099 (2016)."},{"key":"e_1_2_2_61_1","volume-title":"Reddy","author":"Keneshloo Yaser","year":"2019","unstructured":"Yaser Keneshloo, Naren Ramakrishnan, and Chandan K. Reddy. 2019. Deep transfer reinforcement learning for text summarization. In Proceedings of the 2019 SIAM International Conference on Data Mining. SIAM, 675--683."},{"key":"e_1_2_2_62_1","volume-title":"Reddy","author":"Keneshloo Yaser","year":"2018","unstructured":"Yaser Keneshloo, Tian Shi, Naren Ramakrishnan, and Chandan K. Reddy. 2018. Deep reinforcement learning on sequence to sequence models. arXiv preprint arXiv: (2018)."},{"key":"e_1_2_2_63_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Kim Byeongchang","year":"2019","unstructured":"Byeongchang Kim, Hyunwoo Kim, and Gunhee Kim. 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). 2519--2531."},{"key":"e_1_2_2_64_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_2_2_65_1","volume-title":"Kingma and Max Welling","author":"Diederik","year":"2013","unstructured":"Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-4012"},{"key":"e_1_2_2_67_1","volume-title":"Wikihow: A large scale text summarization dataset. arXiv preprint arXiv:1810.09305","author":"Koupaee Mahnaz","year":"2018","unstructured":"Mahnaz Koupaee and William Yang Wang. 2018. Wikihow: A large scale text summarization dataset. arXiv preprint arXiv:1810.09305 (2018)."},{"key":"e_1_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.356"},{"key":"e_1_2_2_69_1","volume-title":"Hinton","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems. 1097--1105."},{"key":"e_1_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1051"},{"key":"e_1_2_2_71_1","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 1808--1817","author":"Kry\u015bci\u0144ski Wojciech","year":"2018","unstructured":"Wojciech Kry\u015bci\u0144ski, Romain Paulus, Caiming Xiong, and Richard Socher. 2018. Improving abstraction in text summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 1808--1817."},{"key":"e_1_2_2_72_1","volume-title":"Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461","author":"Lewis Mike","year":"2019","unstructured":"Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)."},{"key":"e_1_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2009"},{"key":"e_1_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1014"},{"key":"e_1_2_2_75_1","volume-title":"Mutual information and diverse decoding improve neural machine translation. arXiv preprint arXiv:1601.00372","author":"Li Jiwei","year":"2016","unstructured":"Jiwei Li and Dan Jurafsky. 2016. Mutual information and diverse decoding improve neural machine translation. arXiv preprint arXiv:1601.00372 (2016)."},{"key":"e_1_2_2_76_1","volume-title":"A simple, fast diverse decoding algorithm for neural generation. arXiv preprint arXiv:1611.08562","author":"Li Jiwei","year":"2016","unstructured":"Jiwei Li, Will Monroe, and Dan Jurafsky. 2016. A simple, fast diverse decoding algorithm for neural generation. arXiv preprint arXiv:1611.08562 (2016)."},{"key":"e_1_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1127"},{"key":"e_1_2_2_78_1","volume-title":"Actor-critic based training framework for abstractive summarization. arXiv preprint arXiv:1803.11070","author":"Li Piji","year":"2018","unstructured":"Piji Li, Lidong Bing, and Wai Lam. 2018. Actor-critic based training framework for abstractive summarization. arXiv preprint arXiv:1803.11070 (2018)."},{"key":"e_1_2_2_79_1","volume-title":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2091--2100","author":"Li Piji","year":"2017","unstructured":"Piji Li, Wai Lam, Lidong Bing, and Zihao Wang. 2017. Deep recurrent generative decoder for abstractive text summarization. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2091--2100."},{"key":"e_1_2_2_80_1","volume-title":"ROUGE: A package for automatic evaluation of summaries. Text Summarization Branches Out","author":"Lin Chin-Yew","year":"2004","unstructured":"Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. Text Summarization Branches Out (2004)."},{"key":"e_1_2_2_81_1","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 163--169","author":"Lin Junyang","year":"2018","unstructured":"Junyang Lin, Xu Sun, Shuming Ma, and Qi Su. 2018. Global encoding for abstractive summarization. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 163--169."},{"key":"e_1_2_2_82_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-4505"},{"key":"e_1_2_2_83_1","volume-title":"Generative adversarial network for abstractive text summarization. arXiv preprint arXiv:1711.09357","author":"Liu Linqing","year":"2017","unstructured":"Linqing Liu, Yao Lu, Min Yang, Qiang Qu, Jia Zhu, and Hongyan Li. 2017. Generative adversarial network for abstractive text summarization. arXiv preprint arXiv:1711.09357 (2017)."},{"key":"e_1_2_2_84_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1441"},{"key":"e_1_2_2_85_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1387"},{"key":"e_1_2_2_86_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)."},{"key":"e_1_2_2_87_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-011-9216-z"},{"key":"e_1_2_2_88_1","volume-title":"Generating news headlines with recurrent neural networks. arXiv preprint arXiv:1512.01712","author":"Lopyrev Konstantin","year":"2015","unstructured":"Konstantin Lopyrev. 2015. Generating news headlines with recurrent neural networks. arXiv preprint arXiv:1512.01712 (2015)."},{"key":"e_1_2_2_89_1","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1412--1421","author":"Luong Thang","unstructured":"Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1412--1421."},{"key":"e_1_2_2_90_1","volume-title":"Maybury","author":"Mani Inderjeet","year":"1999","unstructured":"Inderjeet Mani and Mark T. Maybury. 1999. Advances in Automatic Text Summarization. MIT press."},{"key":"e_1_2_2_91_1","volume-title":"On faithfulness and factuality in abstractive summarization. arXiv preprint arXiv:2005.00661","author":"Maynez Joshua","year":"2020","unstructured":"Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020. On faithfulness and factuality in abstractive summarization. arXiv preprint arXiv:2005.00661 (2020)."},{"key":"e_1_2_2_92_1","volume-title":"Caiming Xiong, and Richard Socher.","author":"McCann Bryan","year":"2018","unstructured":"Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2018. The natural language Decathlon: Multitask learning as question answering. arXiv preprint arXiv:1806.08730 (2018)."},{"key":"e_1_2_2_93_1","volume-title":"Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 319--328","author":"Miao Yishu","year":"2016","unstructured":"Yishu Miao and Phil Blunsom. 2016. Language as a latent variable: Discrete generative models for sentence compression. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 319--328."},{"key":"e_1_2_2_94_1","volume-title":"Proceedings of the 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). IEEE, 167--174","author":"Miao Yajie","year":"2015","unstructured":"Yajie Miao, Mohammad Gowayyed, and Florian Metze. 2015. EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding. In Proceedings of the 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). IEEE, 167--174."},{"key":"e_1_2_2_95_1","volume-title":"Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing.","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."},{"key":"e_1_2_2_96_1","volume-title":"Proceedings of the 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT). IEEE, 1--7.","author":"Moratanch N.","unstructured":"N. Moratanch and S. Chitrakala. 2016. A survey on abstractive text summarization. In Proceedings of the 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT). IEEE, 1--7."},{"key":"e_1_2_2_97_1","volume-title":"Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. 3075--3081","author":"Nallapati Ramesh","year":"2017","unstructured":"Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017. SummaRuNNer: A recurrent neural network based sequence model for extractive summarization of documents. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. 3075--3081."},{"key":"e_1_2_2_98_1","first-page":"280","article-title":"Abstractive text summarization using sequence-to-sequence RNNs and beyond","volume":"2016","author":"Nallapati Ramesh","year":"2016","unstructured":"Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, \u00c7a glar Gul\u00e7ehre, and Bing Xiang. 2016. Abstractive text summarization using sequence-to-sequence RNNs and beyond. CoNLL 2016 (2016), 280.","journal-title":"CoNLL"},{"key":"e_1_2_2_99_1","doi-asserted-by":"publisher","DOI":"10.5555\/2391200.2391218"},{"key":"e_1_2_2_100_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1206"},{"key":"e_1_2_2_101_1","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","volume":"1","author":"Nema Preksha","year":"2017","unstructured":"Preksha Nema, Mitesh M. Khapra, Anirban Laha, and Balaraman Ravindran. 2017. Diversity driven attention model for query-based abstractive summarization. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 1063--1072."},{"key":"e_1_2_2_102_1","doi-asserted-by":"crossref","unstructured":"Ani Nenkova and Kathleen McKeown. 2011. Automatic Summarization. Now Publishers Inc.","DOI":"10.1561\/9781601984715"},{"key":"e_1_2_2_103_1","doi-asserted-by":"publisher","DOI":"10.3115\/1075096.1075117"},{"key":"e_1_2_2_104_1","unstructured":"Lawrence Page Sergey Brin Rajeev Motwani and Terry Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab."},{"key":"e_1_2_2_105_1","volume-title":"Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 311--318","author":"Papineni Kishore","year":"2002","unstructured":"Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: A method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 311--318."},{"key":"e_1_2_2_106_1","volume-title":"Proceedings of the International Conference on Machine Learning. 1310--1318","author":"Pascanu Razvan","year":"2013","unstructured":"Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013. On the difficulty of training recurrent neural networks. In Proceedings of the International Conference on Machine Learning. 1310--1318."},{"key":"e_1_2_2_107_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2102"},{"key":"e_1_2_2_108_1","volume-title":"Proceedings of the Workshop on New Frontiers in Summarization. 27--32","author":"Pasunuru Ramakanth","year":"2017","unstructured":"Ramakanth Pasunuru, Han Guo, and Mohit Bansal. 2017. Towards improving abstractive summarization via entailment generation. In Proceedings of the Workshop on New Frontiers in Summarization. 27--32."},{"key":"e_1_2_2_109_1","volume-title":"A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304","author":"Paulus Romain","year":"2017","unstructured":"Romain Paulus, Caiming Xiong, and Richard Socher. 2017. A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304 (2017)."},{"key":"e_1_2_2_110_1","volume-title":"Proceedings of the International Conference on Inventive Computing and Informatics (ICICI). IEEE, 762--765","author":"Rachabathuni Pavan Kartheek","year":"2017","unstructured":"Pavan Kartheek Rachabathuni. 2017. A survey on abstractive summarization techniques. In Proceedings of the International Conference on Inventive Computing and Informatics (ICICI). IEEE, 762--765."},{"key":"e_1_2_2_111_1","doi-asserted-by":"publisher","DOI":"10.1162\/089120102762671927"},{"key":"e_1_2_2_112_1","volume-title":"Liu","author":"Raffel Colin","year":"2019","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019)."},{"key":"e_1_2_2_113_1","volume-title":"Sequence level training with recurrent neural networks. arXiv preprint arXiv:1511.06732","author":"Ranzato Marc\u2019Aurelio","year":"2015","unstructured":"Marc\u2019Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2015. Sequence level training with recurrent neural networks. arXiv preprint arXiv:1511.06732 (2015)."},{"key":"e_1_2_2_114_1","volume-title":"Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 1179--1195","author":"Rennie Steven J.","year":"2017","unstructured":"Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel. 2017. Self-critical sequence training for image captioning. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 1179--1195."},{"key":"e_1_2_2_115_1","volume-title":"Proceedings of the International Conference on Machine Learning. 1278--1286","author":"Rezende Danilo Jimenez","year":"2014","unstructured":"Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. In Proceedings of the International Conference on Machine Learning. 1278--1286."},{"key":"e_1_2_2_116_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1044"},{"key":"e_1_2_2_117_1","volume-title":"Multi-source, Multilingual Information Extraction and Summarization","author":"Saggion Horacio","unstructured":"Horacio Saggion and Thierry Poibeau. 2013. Automatic text summarization: Past, present and future. In Multi-source, Multilingual Information Extraction and Summarization. Springer, 3--21."},{"key":"e_1_2_2_118_1","volume-title":"Temporal attention model for neural machine translation. CoRR abs\/1608.02927","author":"Sankaran Baskaran","year":"2016","unstructured":"Baskaran Sankaran, Haitao Mi, Yaser Al-Onaizan, and Abe Ittycheriah. 2016. Temporal attention model for neural machine translation. CoRR abs\/1608.02927 (2016)."},{"key":"e_1_2_2_119_1","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 1073--1083","author":"Liu Peter J.","unstructured":"Abigail See, Peter J. Liu, and Christopher D. Manning. 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). Association for Computational Linguistics, 1073--1083."},{"key":"e_1_2_2_120_1","volume-title":"Parikh","author":"Sellam Thibault","year":"2020","unstructured":"Thibault Sellam, Dipanjan Das, and Ankur P. Parikh. 2020. BLEURT: Learning robust metrics for text generation. arXiv preprint arXiv:2004.04696 (2020)."},{"key":"e_1_2_2_121_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1212"},{"key":"e_1_2_2_122_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1159"},{"key":"e_1_2_2_123_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-017-1758-3"},{"key":"e_1_2_2_124_1","volume-title":"Reddy","author":"Shi Tian","year":"2019","unstructured":"Tian Shi, Ping Wang, and Chandan K. Reddy. 2019. LeafNATS: An open-source toolkit and live demo system for neural abstractive text summarization. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). 66--71."},{"key":"e_1_2_2_125_1","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, 1717--1729","author":"Song Kaiqiang","year":"2018","unstructured":"Kaiqiang Song, Lin Zhao, and Fei Liu. 2018. Structure-infused copy mechanisms for abstractive summarization. In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, 1717--1729."},{"key":"e_1_2_2_126_1","volume-title":"Training Recurrent Neural Networks","author":"Sutskever Ilya","unstructured":"Ilya Sutskever. 2013. Training Recurrent Neural Networks. University of Toronto Toronto, Ontario, Canada."},{"key":"e_1_2_2_127_1","volume-title":"Le","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence-to-sequence learning with neural networks. In Advances in Neural Information Processing Systems. 3104--3112."},{"key":"e_1_2_2_128_1","volume-title":"Barto","author":"Sutton Richard S.","year":"1998","unstructured":"Richard S. Sutton and Andrew G. Barto. 1998. Reinforcement Learning: An Introduction. MIT press."},{"key":"e_1_2_2_129_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1112"},{"key":"e_1_2_2_130_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1108"},{"key":"e_1_2_2_131_1","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","volume":"1","author":"Tu Zhaopeng","year":"2016","unstructured":"Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016. Modeling coverage for neural machine translation. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 76--85."},{"key":"e_1_2_2_132_1","volume-title":"Proceedings of the 9th ISCA Speech Synthesis Workshop. 125--125","author":"van den Oord A\u00e4ron","unstructured":"A\u00e4ron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. [n.d.]. WaveNet: A generative model for raw audio. In Proceedings of the 9th ISCA Speech Synthesis Workshop. 125--125."},{"key":"e_1_2_2_133_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 6000--6010."},{"key":"e_1_2_2_134_1","volume-title":"Proceedings of the 29th AAAI Conference on Artificial Intelligence.","author":"Venkatraman Arun","unstructured":"Arun Venkatraman, Martial Hebert, and J. Andrew Bagnell. 2015. Improving multi-step prediction of learned time series models. In Proceedings of the 29th AAAI Conference on Artificial Intelligence."},{"key":"e_1_2_2_135_1","volume-title":"Verma and Daniel Lee","author":"Rakesh","year":"2017","unstructured":"Rakesh M. Verma and Daniel Lee. 2017. Extractive summarization: Limits, compression, generalized model and heuristics. Computaci\u00f3n y Sistemas 21 (2017)."},{"key":"e_1_2_2_136_1","volume-title":"Diverse beam search: Decoding diverse solutions from neural sequence models. arXiv preprint arXiv:1610.02424","author":"Vijayakumar Ashwin K.","year":"2016","unstructured":"Ashwin K. Vijayakumar, Michael Cogswell, Ramprasath R. Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. 2016. Diverse beam search: Decoding diverse solutions from neural sequence models. arXiv preprint arXiv:1610.02424 (2016)."},{"key":"e_1_2_2_137_1","unstructured":"Oriol Vinyals Meire Fortunato and Navdeep Jaitly. 2015. Pointer networks. In Advances in Neural Information Processing Systems. 2692--2700."},{"key":"e_1_2_2_138_1","volume-title":"Proceedings of the 27th International Joint Conference on Artificial Intelligence. AAAI Press, 4453--4460","author":"Wang Li","year":"2018","unstructured":"Li Wang, Junlin Yao, Yunzhe Tao, Li Zhong, Wei Liu, and Qiang Du. 2018. A reinforced topic-aware convolutional sequence-to-sequence model for abstractive text summarization. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. AAAI Press, 4453--4460."},{"key":"e_1_2_2_139_1","volume-title":"Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc., 538--545","author":"Weaver Lex","year":"2001","unstructured":"Lex Weaver and Nigel Tao. 2001. The optimal reward baseline for gradient-based reinforcement learning. In Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc., 538--545."},{"key":"e_1_2_2_140_1","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics","author":"Liu Fei","year":"2019","unstructured":"Fei Liu, Yang Gao, Christian M. Meyer, Steffen Eger, Wei Zhao, and Maxime Peyrard. 2019. MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Hong Kong, China."},{"key":"e_1_2_2_141_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.58337"},{"key":"e_1_2_2_142_1","volume-title":"Reinforcement Learning","author":"Williams Ronald J.","unstructured":"Ronald J. Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. In Reinforcement Learning. Springer, 5--32."},{"key":"e_1_2_2_143_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.2.270"},{"key":"e_1_2_2_144_1","volume-title":"32nd AAAI Conference on Artificial Intelligence (AAAI'18)","author":"Wu Yuxiang","year":"2018","unstructured":"Yuxiang Wu and Baotian Hu. 2018. Learning to extract coherent summary via deep reinforcement learning. In 32nd AAAI Conference on Artificial Intelligence (AAAI'18). 5602."},{"key":"e_1_2_2_145_1","volume-title":"Google\u2019s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144","author":"Wu Yonghui","year":"2016","unstructured":"Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, \u0141ukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016. Google\u2019s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)."},{"key":"e_1_2_2_146_1","unstructured":"Yingce Xia Fei Tian Lijun Wu Jianxin Lin Tao Qin Nenghai Yu and Tie-Yan Liu. 2017. Deliberation networks: Sequence generation beyond one-pass decoding. In Advances in Neural Information Processing Systems. 1782--1792."},{"key":"e_1_2_2_147_1","volume-title":"Proceedings of the International Conference on Machine Learning. 2048--2057","author":"Xu Kelvin","year":"2015","unstructured":"Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In Proceedings of the International Conference on Machine Learning. 2048--2057."},{"key":"e_1_2_2_148_1","volume-title":"ProphetNet: Predicting future N-gram for sequence-to-sequence pre-training. arXiv preprint arXiv:2001.04063","author":"Yan Yu","year":"2020","unstructured":"Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, and Ming Zhou. 2020. ProphetNet: Predicting future N-gram for sequence-to-sequence pre-training. arXiv preprint arXiv:2001.04063 (2020)."},{"key":"e_1_2_2_149_1","volume-title":"Le","author":"Yang Zhilin","year":"2019","unstructured":"Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R. Salakhutdinov, and Quoc V. Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. In Advances in Neural Information Processing Systems. 5754--5764."},{"key":"e_1_2_2_150_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1174"},{"key":"e_1_2_2_151_1","volume-title":"Reinforcement learning neural turing machines-revised. arXiv preprint arXiv:1505.00521","author":"Zaremba Wojciech","year":"2015","unstructured":"Wojciech Zaremba and Ilya Sutskever. 2015. Reinforcement learning neural turing machines-revised. arXiv preprint arXiv:1505.00521 (2015)."},{"key":"e_1_2_2_152_1","volume-title":"Efficient summarization with read-again and copy mechanism. arXiv preprint arXiv:1611.03382","author":"Zeng Wenyuan","year":"2016","unstructured":"Wenyuan Zeng, Wenjie Luo, Sanja Fidler, and Raquel Urtasun. 2016. Efficient summarization with read-again and copy mechanism. arXiv preprint arXiv:1611.03382 (2016)."},{"key":"e_1_2_2_153_1","volume-title":"Liu","author":"Zhang Jingqing","year":"2019","unstructured":"Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2019. PEGASUS: Pre-training with extracted gap-sentences for abstractive summarization. arXiv preprint arXiv:1912.08777 (2019)."},{"key":"e_1_2_2_154_1","volume-title":"Bertscore: Evaluating text generation with Bert. arXiv preprint arXiv:1904.09675","author":"Zhang Tianyi","year":"2019","unstructured":"Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with Bert. arXiv preprint arXiv:1904.09675 (2019)."},{"key":"e_1_2_2_155_1","volume-title":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 584--594","author":"Zhang Xingxing","year":"2017","unstructured":"Xingxing Zhang and Mirella Lapata. 2017. Sentence simplification with deep reinforcement learning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 584--594."},{"key":"e_1_2_2_156_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1088"},{"key":"e_1_2_2_157_1","volume-title":"Proceedings of EMNLP 2018","author":"Zhang Yuhao","year":"2018","unstructured":"Yuhao Zhang, Daisy Yi Ding, Tianpei Qian, Christopher D. Manning, and Curtis P. Langlotz. 2018. Learning to summarize radiology findings. In Proceedings of EMNLP 2018 (2018), 204."},{"key":"e_1_2_2_158_1","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","volume":"1","author":"Zhou Qingyu","year":"2018","unstructured":"Qingyu Zhou, Nan Yang, Furu Wei, Shaohan Huang, Ming Zhou, and Tiejun Zhao. 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), Vol. 1. 654--663."},{"key":"e_1_2_2_159_1","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","volume":"1","author":"Zhou Qingyu","year":"2017","unstructured":"Qingyu Zhou, Nan Yang, Furu Wei, and Ming Zhou. 2017. Selective encoding for abstractive sentence summarization. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 1095--1104."}],"container-title":["ACM\/IMS Transactions on Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3419106","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3419106","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T13:57:35Z","timestamp":1776347855000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3419106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,3]]},"references-count":159,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,2,28]]}},"alternative-id":["10.1145\/3419106"],"URL":"https:\/\/doi.org\/10.1145\/3419106","relation":{},"ISSN":["2691-1922"],"issn-type":[{"value":"2691-1922","type":"print"}],"subject":[],"published":{"date-parts":[[2021,1,3]]},"assertion":[{"value":"2019-06-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-08-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-01-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}