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Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition.
Hierarchical grouping to optimize an objective function
Joe H. Ward. 1963 · 1963
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Rhetorical structure theory: Toward a functional theory of text organization
William C. Mann and Sandra A. Thompson. 1988 · 1988
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The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries
Jaime Carbonell and Jade Goldstein. 1998 · 1998
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Lexrank: Graph-based lexical centrality as salience in text summarization
Günes Erkan and Dragomir R. Radev. 2004 · 2004
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Centroid-based summarization of multiple documents
Dragomir R. Radev, Hongyan Jing, Magorzata Sty, and Daniel Tam. 2004 · 2004
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Beyond sumbasic: Task-focused summarization with sentence simplification and lexical expansion
Lucy Vanderwende, Hisami Suzuki, Chris Brockett, and Ani Nenkova. 2007 · 2007
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Multi-document summarization using cluster-based link analysis
Xiaojun Wan and Jianwu Yang. 2008 · 2008
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Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization
Dingding Wang, Tao Li, Shenghuo Zhu, and C. Ding. 2008 · 2008
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Exploring content models for multi-document summarization
Aria Haghighi and Lucy Vanderwende. 2009 · 2009
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Simultaneous ranking and clustering of sentences: A reinforcement approach to multi-document summarization
Xiaoyan Cai, Wenjie Li, You Ouyang, and Hong Yan. 2010 · 2010
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Opinosis: A graph based approach to abstractive summarization of highly redundant opinions
Kavita Ganesan, ChengXiang Zhai, and Jiawei Han. 2010 · 2010
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Integrating document clustering and multidocument summarization
Dingding Wang, Shenghuo Zhu, Tao Li, Yun Chi, and Yihong Gong. 2011 · 2011
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Ranking through clustering: An integrated approach to multi-document summarization
X. Cai and Wenjie Li. 2013 · 2013
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Abstractive multi-document summarization via phrase selection and merging
Lidong Bing, Piji Li, Yi Liao, Wai Lam, Weiwei Guo, and Rebecca Passonneau. 2015 · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Clustering sentences with density peaks for multi-document summarization
Yang Zhang, Yunqing Xia, Yi Liu, and Wenmin Wang. 2015 · 2015
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The role of discourse units in near-extractive summarization
Junyi Jessy Li, Kapil Thadani, and Amanda Stent. 2016 · 2016
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Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017 · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
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Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model
Alexander Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir Radev. 2019 · 2019
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Neural sentence fusion for diversity driven abstractive multi-document summarization
Tanvir Ahmed Fuad, Mir Tafseer Nayeem, Asif Mahmud, and Yllias Chali. 2019 · 2019
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Scoring sentence singletons and pairs for abstractive summarization
Logan Lebanoff, Kaiqiang Song, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, and Fei Liu. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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Exploiting discourse-level segmentation for extractive summarization
Zhengyuan Liu and Nancy Chen. 2019 · 2019
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Better highlighting: Creating sub-sentence summary highlights
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Ori Shapira, Hadar Ronen, Meni Adler, Yael Amsterdamer, Judit Bar-Ilan, and Ido Dagan. 2017 · 2017
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Yen-Chun Chen and Mohit Bansal. 2018 · 2018
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Adapting the neural encoder-decoder framework from single to multi-document summarization
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Abstractive unsupervised multi-document summarization using paraphrastic sentence fusion
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Structure-infused copy mechanisms for abstractive summarization
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Supervised open information extraction
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Abstractive multi-document summarization via joint learning with single-document summarization
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
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Multi-document summarization with maximal marginal relevance-guided reinforcement learning
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Discourse-aware neural extractive text summarization
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