{"type":"ScholarlyArticle","title":"From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information","authors":[{"name":"Shen Gao","slug":"shen-gao","given":"Shen","family":"Gao","affiliation":"Wangxuan Institute of Computer Technology , Peking University , Beijing , China"},{"name":"Xiuying Chen","slug":"xiuying-chen","given":"Xiuying","family":"Chen","affiliation":"Wangxuan Institute of Computer Technology , Peking University , Beijing , China"},{"name":"Zhaochun Ren","slug":"zhaochun-ren","given":"Zhaochun","family":"Ren","affiliation":"School of Computer Science and Technology , Shandong University , Qingdao , China"},{"name":"Dongyan Zhao","slug":"dongyan-zhao","given":"Dongyan","family":"Zhao","affiliation":"Wangxuan Institute of Computer Technology , Peking University , Beijing , China"},{"name":"Rui Yan","slug":"rui-yan","given":"Rui","family":"Yan","affiliation":"Wangxuan Institute of Computer Technology , Peking University , Beijing , China; Beijing Academy of Artificial Intelligence"}],"abstract":"Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words.\r\nThis research topic has started to attract the attention of a large community of researchers, and it is nowadays counted as one of the most promising research areas.\r\nIn general, text summarization algorithms aim at using a plain text document as input and then output a summary.\r\nHowever, in real-world applications, most of the data is not in a plain text format.\r\nInstead, there is much manifold information to be summarized, such as the summary for a web page based on a query in the search engine, extreme long document (e.g. academic paper), dialog history and so on.\r\nIn this paper, we focus on the survey of these new summarization tasks and approaches in the real-world application.","doi":"10.24963/ijcai.2020/676","url":"https://ijcai.org/proceedings/2020/676","pdf":"https://ijcai.org/proceedings/2020/0676.pdf","markdown":"https://ijcai.org/proceedings/2020/676.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/676","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Survey track","track":"Survey track","number":676,"pages":{"first":4854,"last":4860},"keywords":["Natural Language Processing: general"],"videos":[],"references":[{"position":1,"raw":"[Allan and et al., 2001] James Allan and et al. Temporal summaries of new topics. In SIGIR, pages 10-18. ACM, 2001.","title":"Temporal summaries of new topics","authors":"Allan","year":"2001","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Boudin and et al., 2008] Florian Boudin and et al. A scalable mmr approach to sentence scoring for multi-document update summa- rization. In COLING, 2008.","title":"A scalable mmr approach to sentence scoring for multi-document update summarization","authors":"Boudin","year":"2008","venue":"COLING","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cao et al., 2018] Ziqiang Cao, Wenjie Li, Sujian Li, and Furu Wei. Retrieve, rerank and rewrite: Soft template based neural summa- rization. In ACL, 2018.","title":"Retrieve, rerank and rewrite: Soft template based neural summarization","authors":"Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei","year":"2018","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Celikyilmaz and et al., 2018] Asli Celikyilmaz and et al. Deep communicating agents for abstractive summarization. In NAACL, 2018.","title":"Asli Celikyilmaz and et al. Deep communicating agents for abstractive summarization","authors":"Celikyilmaz","year":"2018","venue":"NAACL","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen and Zhuge, 2018] Jingqiang Chen and Hai Zhuge. Abstrac- tive text-image summarization using multi-modal attentional hi- erarchical rnn. In EMNLP, 2018.","title":"Abstractive text-image summarization using multi-modal attentional hierarchical rnn","authors":"Chen, Zhuge ;, Jingqiang Chen, Hai Zhuge","year":"2018","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chen et al., 2018] Xiuying Chen, Shen Gao, Chongyang Tao, Yan Song, Dongyan Zhao, and Rui Yan. Iterative document rep- resentation learning towards summarization with polishing. In EMNLP, 2018.","title":"Iterative document representation learning towards summarization with polishing","authors":"Xiuying Chen, Shen Gao, Chongyang Tao, Yan Song, Dongyan Zhao, Rui Yan","year":"2018","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2019] Xiuying Chen, Zhangming Chan, Shen Gao, Meng-Hsuan Yu, Dongyan Zhao, and Rui Yan. Learning towards abstractive timeline summarization. In IJCAI, 2019.","title":"Learning towards abstractive timeline summarization","authors":"Xiuying Chen, Zhangming Chan, Shen Gao, Meng-Hsuan Yu, Dongyan Zhao, Rui Yan","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/686"},{"position":8,"raw":"[Cheng and Lapata, 2016] Jianpeng Cheng and Mirella Lapata. Neural summarization by extracting sentences and words. In ACL, pages 484-494, 2016.","title":"Neural summarization by extracting sentences and words","authors":"Lapata Cheng, Jianpeng Cheng, Mirella Lapata","year":"2016","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Cohan and et al., 2018] Arman Cohan and et al. A discourse- aware attention model for abstractive summarization of long doc- uments. In NAACL-HLT, 2018.","title":"A discourseaware attention model for abstractive summarization of long documents","authors":"Arman Cohan","year":"2018","venue":"NAACL-HLT","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Collins et al., 2017] Ed Collins, Isabelle Augenstein, and Sebas- tian Riedel. A supervised approach to extractive summarisation of scientific papers. In CoNLL, pages 195-205, August 2017.","title":"A supervised approach to extractive summarisation of scientific papers","authors":"Collins","year":"2017","venue":"CoNLL","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Dang and Owczarzak, 2008] Hoa Trang Dang and Karolina Owczarzak. Overview of the tac 2008 update summarization task. In TAC, 2008.","title":"Overview of the tac 2008 update summarization task","authors":"Owczarzak ; Dang, Trang Hoa, Karolina Dang, Owczarzak","year":"2008","venue":"TAC","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Delort and Alfonseca, 2012] Jean-Yves Delort and Enrique Alfon- seca. Dualsum: a topic-model based approach for update sum- marization. In EACL, 2012.","title":"Dualsum: a topic-model based approach for update summarization","authors":"Alfonseca ; Jean-Yves Delort, Enrique Delort, Alfonseca","year":"2012","venue":"EACL","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Dorr and et al., 2003] Bonnie J. Dorr and et al. Hedge trimmer: A parse-and-trim approach to headline generation. In HLT-NAACL, 2003.","title":"Hedge trimmer: A parse-and-trim approach to headline generation","authors":"Bonnie J Dorr","year":"2003","venue":"HLT-NAACL","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Feigenblat and et al., 2017] Guy Feigenblat and et al. Unsuper- vised query-focused multi-document summarization using the cross entropy method. In SIGIR, 2017.","title":"Guy Feigenblat and et al. Unsupervised query-focused multi-document summarization using the cross entropy method","authors":"Feigenblat","year":"2017","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Ganesh and Dingliwal, 2019] Prakhar Ganesh and Saket Dingli- wal. Abstractive summarization of spoken and written conver- sation. ArXiv, abs/1902.01615, 2019.","title":"Abstractive summarization of spoken and written conversation","authors":"Prakhar Ganesh, Saket Dingliwal","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Gao et al., 2019a] Shen Gao, Xiuying Chen, Piji Li, Zhangming Chan, Dongyan Zhao, and Rui Yan. How to write summaries with patterns? learning towards abstractive summarization through prototype editing. In EMNLP, 2019.","title":"How to write summaries with patterns? learning towards abstractive summarization through prototype editing","authors":"Gao","year":"2019","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Gao et al., 2019b] Shen Gao, Xiuying Chen, Piji Li, Zhaochun Ren, Lidong Bing, Dongyan Zhao, and Rui Yan. Abstractive text summarization by incorporating reader comments. In AAAI, 2019.","title":"Abstractive text summarization by incorporating reader comments","authors":"Gao","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Gao et al., 2019c] Shen Gao, Zhaochun Ren, Yihong Zhao, Dongyan Zhao, Dawei Yin, and Rui Yan. Product-aware answer generation in e-commerce question-answering. In WSDM, 2019.","title":"Product-aware answer generation in e-commerce question-answering","authors":"Gao","year":"2019","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ge and et al., 2016] Tao Ge and et al. News stream summarization using burst information networks. In EMNLP, 2016.","title":"Tao Ge and et al. News stream summarization using burst information networks","authors":"Ge","year":"2016","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Gillick and et al., 2009] Dan Gillick and et al. A scalable global model for summarization. In ILP 2009, 2009.","title":"A scalable global model for summarization","authors":"Gillick","year":"2009","venue":"ILP 2009","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Gliwa and et al., 2019] Bogdan Gliwa and et al. Samsum corpus: A human-annotated dialogue dataset for abstractive summariza- tion. In EMNLP, 2019.","title":"Samsum corpus: A human-annotated dialogue dataset for abstractive summarization","authors":"Bogdan Gliwa","year":"2019","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Gu and et al., 2016] Jiatao Gu and et al. Incorporating copying mechanism in sequence-to-sequence learning. Acl, page 11, 2016.","title":"Jiatao Gu and et al. Incorporating copying mechanism in sequence-to-sequence learning","authors":"Gu","year":"2016","venue":"Acl","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Hsu and et al., 2018] Wan-Ting Hsu and et al. A unified model for extractive and abstractive summarization using inconsistency loss. In ACL, 2018.","title":"A unified model for extractive and abstractive summarization using inconsistency loss","authors":"Wan-Ting Hsu","year":"2018","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Hu and et al., 2008] Meishan Hu and et al. Comments-oriented document summarization: understanding documents with read- ers' feedback. In SIGIR, 2008.","title":"Comments-oriented document summarization: understanding documents with readers' feedback","authors":"Hu","year":"2008","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Kikuchi and et al., 2014] Yuta Kikuchi and et al. Single document summarization based on nested tree structure. In ACL, 2014.","title":"Single document summarization based on nested tree structure","authors":"Kikuchi","year":"2014","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Li and et al., 2015] Piji Li and et al. Reader-aware multi-document summarization via sparse coding. In IJCAI, 2015.","title":"Piji Li and et al. Reader-aware multi-document summarization via sparse coding","authors":"Li","year":"2015","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Li and et al., 2017a] Haoran Li and et al. Multi-modal summariza- tion for asynchronous collection of text, image, audio and video. In EMNLP, 2017.","title":"Multi-modal summarization for asynchronous collection of text, image, audio and video","authors":"Li","year":"2017","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Li and et al., 2017b] Piji Li and et al. Reader-aware multi- document summarization: An enhanced model and the first dataset. In NFiS@EMNLP, 2017.","title":"Reader-aware multidocument summarization: An enhanced model and the first dataset","authors":"Li","year":"2017","venue":"NFiS@EMNLP","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Li and et al., 2019] Manling Li and et al. Keep meeting summaries on topic: Abstractive multi-modal meeting summarization. In ACL, 2019.","title":"Keep meeting summaries on topic: Abstractive multi-modal meeting summarization","authors":"Manling Li","year":"2019","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Li and Li, 2014] Yanran Li and Sujian Li. Query-focused multi- document summarization: Combining a topic model with graph- based semi-supervised learning. In COLING, pages 1197-1207, August 2014.","title":"Query-focused multidocument summarization: Combining a topic model with graphbased semi-supervised learning","authors":"Li ; Li, Yanran Li, Sujian Li","year":"2014","venue":"COLING","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Li et al., 2018] Haoran Li, Junnan Zhu, Tianshang Liu, Jiajun Zhang, and Chengqing Zong. Multi-modal sentence summariza- tion with modality attention and image filtering. In IJCAI, 2018.","title":"Multi-modal sentence summarization with modality attention and image filtering","authors":"Haoran Li, Junnan Zhu, Tianshang Liu, Jiajun Zhang, Chengqing Zong","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/577"},{"position":32,"raw":"[Li et al., 2019] Juntao Li, Lidong Bing, Lisong Qiu, Dongmin Chen, Dongyan Zhao, and Rui Yan. Learning to write stories with thematic consistency and wording novelty. In AAAI, 2019.","title":"Learning to write stories with thematic consistency and wording novelty","authors":"Juntao Li, Lidong Bing, Lisong Qiu, Dongmin Chen, Dongyan Zhao, Rui Yan","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Lin, 2004] Chin-Yew Lin. ROUGE: A package for automatic eval- uation of summaries. In Text Summarization Branches Out, pages 74-81, July 2004.","title":"ROUGE: A package for automatic evaluation of summaries","authors":"Chin-Yew Lin","year":"2004","venue":"Text Summarization Branches Out","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Liu and et al., 2018a] Linqing Liu and et al. Generative adversarial network for abstractive text summarization. In AAAI, 2018.","title":"Generative adversarial network for abstractive text summarization","authors":"Liu","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Liu and et al., 2018b] Peter J. Liu and et al. Generating wikipedia by summarizing long sequences. In ICLR, 2018.","title":"Generating wikipedia by summarizing long sequences","authors":"Liu","year":"2018","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Liu and et al., 2019] Chunyi Liu and et al. Automatic dialogue summary generation for customer service. In KDD, 2019.","title":"Automatic dialogue summary generation for customer service","authors":"Liu","year":"2019","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Liu and Lapata, 2019] Yang Liu and Mirella Lapata. Text summa- rization with pretrained encoders. In EMNLP, 2019.","title":"Text summarization with pretrained encoders","authors":"Yang Liu, Mirella Lapata","year":"2019","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Mnasri and et al., 2017] Maali Mnasri and et al. Taking into ac- count inter-sentence similarity for update summarization. In IJC- NLP, 2017.","title":"Maali Mnasri and et al. Taking into account inter-sentence similarity for update summarization","authors":"Mnasri","year":"2017","venue":"IJC-NLP","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Morita and et al., 2013] Hajime Morita and et al. Subtree extrac- tive summarization via submodular maximization. In ACL, 2013.","title":"Subtree extractive summarization via submodular maximization","authors":"Morita","year":"2013","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Nallapati and et al., 2016] Ramesh Nallapati and et al. Abstractive text summarization using sequence-to-sequence rnns and beyond. In CoNLL, 2016.","title":"Abstractive text summarization using sequence-to-sequence rnns and beyond","authors":"Ramesh Nallapati","year":"2016","venue":"CoNLL","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Nallapati et al., 2017] Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. Summarunner: A recurrent neural network based sequence model for extractive summarization of documents. In AAAI, 2017.","title":"Summarunner: A recurrent neural network based sequence model for extractive summarization of documents","authors":"Ramesh Nallapati, Feifei Zhai, Bowen Zhou","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Narayan and et al., 2018] Shashi Narayan and et al. Ranking sen- tences for extractive summarization with reinforcement learning. In NAACL, 2018.","title":"Ranking sentences for extractive summarization with reinforcement learning","authors":"Narayan","year":"2018","venue":"NAACL","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Nema and et al., 2017] Preksha Nema and et al. Diversity driven attention model for query-based abstractive summarization. In ACL, 2017.","title":"Preksha Nema and et al. Diversity driven attention model for query-based abstractive summarization","authors":"Nema","year":"2017","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Oya and et al., 2014] Tatsuro Oya and et al. A template-based abstractive meeting summarization: Leveraging summary and source text relationships. In INLG, 2014.","title":"A template-based abstractive meeting summarization: Leveraging summary and source text relationships","authors":"Oya","year":"2014","venue":"INLG","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Palaskar and et al., 2019] Shruti Palaskar and et al. Multimodal abstractive summarization for how2 videos. In ACL, 2019.","title":"Shruti Palaskar and et al. Multimodal abstractive summarization for how2 videos","authors":"Palaskar","year":"2019","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Paulus and et al., 2018] Romain Paulus and et al. A deep rein- forced model for abstractive summarization. In ICLR, 2018.","title":"A deep reinforced model for abstractive summarization","authors":"Romain Paulus","year":"2018","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Qiu et al., 2019] Lisong Qiu, Juntao Li, Wei Bi, Dongyan Zhao, and Rui Yan. Are training samples correlated? learning to gener- ate dialogue responses with multiple references. In ACL, 2019.","title":"Are training samples correlated? learning to generate dialogue responses with multiple references","authors":"Qiu","year":"2019","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Radev and et al., 2004] Dragomir R. Radev and et al. Centroid- based summarization of multiple documents. volume 40, pages 919-938, 2004.","title":"Centroidbased summarization of multiple documents","authors":"R Dragomir, Radev","year":"2004","venue":null,"doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Ren et al., 2013] Zhaochun Ren, Shangsong Liang, Edgar Meij, and Maarten de Rijke. Personalized time-aware tweets summa- rization. In SIGIR, pages 513-522. ACM, 2013.","title":"Personalized time-aware tweets summarization","authors":"Ren","year":"2013","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[See et al., 2017] Abigail See, Peter J. Liu, and Christopher D. Manning. Get to the point: Summarization with pointer- generator networks. In ACL, 2017.","title":"Get to the point: Summarization with pointergenerator networks","authors":"Abigail See, Peter J Liu, Christopher D Manning","year":"2017","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Shang and et al., 2018] Guokan Shang and et al. Unsupervised ab- stractive meeting summarization with multi-sentence compres- sion and budgeted submodular maximization. In ACL, 2018.","title":"Guokan Shang and et al. Unsupervised abstractive meeting summarization with multi-sentence compression and budgeted submodular maximization","authors":"Shang","year":"2018","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Sharma et al., 2019] Eva Sharma, Chen Li, and Lu Wang. Big- patent: A large-scale dataset for abstractive and coherent sum- marization. In ACL, 2019.","title":"Bigpatent: A large-scale dataset for abstractive and coherent summarization","authors":"Eva Sharma, Chen Li, Lu Wang","year":"2019","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Sutskever et al., 2014] Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. Sequence to sequence learning with neural networks. In NIPS, 2014.","title":"Sequence to sequence learning with neural networks","authors":"Ilya Sutskever, Oriol Vinyals, V Quoc, Le","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Teufel and Moens, 2002] Simone Teufel and Marc Moens. Sum- marizing scientific articles: Experiments with relevance and rhetorical status. Computational Linguistics, 28:409-445, 2002.","title":"Summarizing scientific articles: Experiments with relevance and rhetorical status","authors":"Moens Teufel, Simone Teufel, Marc Moens","year":"2002","venue":"Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Wang and Cardie, 2013] Lu Wang and Claire Cardie. Domain- independent abstract generation for focused meeting summariza- tion. In ACL, pages 1395-1405, August 2013.","title":"Domainindependent abstract generation for focused meeting summarization","authors":"Wang, Cardie, Lu Wang, Claire Cardie","year":"2013","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Wang and et al., 2013] Lu Wang and et al. A sentence compres- sion based framework to query-focused multi-document summa- rization. In ACL, pages 1384-1394, August 2013.","title":"A sentence compression based framework to query-focused multi-document summarization","authors":"Wang","year":"2013","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Wang and et al., 2018] Li Wang and et al. A reinforced topic- aware convolutional sequence-to-sequence model for abstractive text summarization. In IJCAI, 2018.","title":"A reinforced topicaware convolutional sequence-to-sequence model for abstractive text summarization","authors":"Li Wang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Wang et al., 2019] Kai Wang, Xiaojun Quan, and Rui Wang. Biset: Bi-directional selective encoding with template for abstractive summarization. In ACL, pages 2153-2162, July 2019.","title":"Biset: Bi-directional selective encoding with template for abstractive summarization","authors":"Kai Wang, Xiaojun Quan, Rui Wang","year":"2019","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Wu and Hu, 2018] Yuxiang Wu and Baotian Hu. Learning to ex- tract coherent summary via deep reinforcement learning. In AAAI, 2018.","title":"Learning to extract coherent summary via deep reinforcement learning","authors":"Hu ; ] Yuxiang Wu, Baotian Wu, Hu","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Xiao and Carenini, 2019] Wen Xiao and Giuseppe Carenini. Ex- tractive summarization of long documents by combining global and local context. In EMNLP, 2019.","title":"Extractive summarization of long documents by combining global and local context","authors":"Wen Xiao, Giuseppe Carenini","year":"2019","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":61,"raw":"[Yan et al., 2011a] Rui Yan, Liang Kong, Congrui Huang, Xiaojun Wan, Xiaoming Li, and Yan Zhang. Timeline generation through evolutionary trans-temporal summarization. In EMNLP, pages 433-443. ACL, 2011.","title":"Timeline generation through evolutionary trans-temporal summarization","authors":"Yan","year":"2011","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":62,"raw":"[Yan et al., 2011b] Rui Yan, Xiaojun Wan, Jahna Otterbacher, Liang Kong, Xiaoming Li, and Yan Zhang. Evolutionary timeline summarization: a balanced optimization framework via iterative substitution. In SIGIR, pages 745-754. ACM, 2011.","title":"Evolutionary timeline summarization: a balanced optimization framework via iterative substitution","authors":"Yan","year":"2011","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":63,"raw":"[Yan et al., 2012] Rui Yan, Xiaojun Wan, Mirella Lapata, Wayne Xin Zhao, Pu-Jen Cheng, and Xiaoming Li. Visu- alizing timelines: Evolutionary summarization via iterative reinforcement between text and image streams. In CIKM, pages 275-284. ACM, 2012.","title":"Visualizing timelines: Evolutionary summarization via iterative reinforcement between text and image streams","authors":"Rui Yan, Xiaojun Wan, Mirella Lapata, Wayne Xin Zhao, Pu-Jen Cheng, Xiaoming Li","year":"2012","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":64,"raw":"[Yates and et al., 2007] Alexander Yates and et al. Textrunner: Open information extraction on the web. In HLT-NAACL, 2007.","title":"Textrunner: Open information extraction on the web","authors":"Yates","year":"2007","venue":"HLT-NAACL","doi":null,"url":null,"internal_url":null},{"position":65,"raw":"[Zhang et al., 2019] Xingxing Zhang, Furu Wei, and Ming Zhou. Hibert: Document level pre-training of hierarchical bidirectional transformers for document summarization. In ACL, 2019.","title":"Hibert: Document level pre-training of hierarchical bidirectional transformers for document summarization","authors":"Xingxing Zhang, Furu Wei, Ming Zhou","year":"2019","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":66,"raw":"[Zhou and et al., 2017] Qingyu Zhou and et al. Selective encoding for abstractive sentence summarization. In ACL, 2017.","title":"Selective encoding for abstractive sentence summarization","authors":"Zhou","year":"2017","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":67,"raw":"[Zhu et al., 2018] Junnan Zhu, Haoran Li, Tianshang Liu, Yu Zhou, Jiajun Zhang, and Chengqing Zong. Msmo: Multimodal summa- rization with multimodal output. In EMNLP, 2018.","title":"Msmo: Multimodal summarization with multimodal output","authors":"Junnan Zhu, Haoran Li, Tianshang Liu, Yu Zhou, Jiajun Zhang, Chengqing Zong","year":"2018","venue":"EMNLP","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}