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Summarization is the task of compressing source document(s) into coherent and succinct passages.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2019 · 1912
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The use of mmr, diversity-based reranking for reordering documents and producing summaries
Jaime G Carbonell and Jade Goldstein. 1998 · 1998
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An investigation of practical approximate nearest neighbor algorithms
Ting Liu, Andrew W. Moore, Alexander G. Gray, and Ke Yang. 2004 · 2004
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TextRank: Bringing order into text
Rada Mihalcea and Paul Tarau. 2004 · 2004
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Duc 2005: Evaluation of question-focused summarization systems
Hoa Trang Dang. 2006 · 2005
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Bayesian query-focused summarization
Hal Daumé III and Daniel Marcu. 2006 · 2006
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Fastsum: Fast and accurate query-based multi-document summarization
Frank Schilder and Ravikumar Kondadadi. 2008 · 2008
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Biased LexRank: Passage retrieval using random walks with question-based priors
Jahna Otterbacher, Gunes Erkan, and Dragomir R. Radev. 2009 · 2009
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Discourse constraints for document compression
James Clarke and Mirella Lapata. 2010 · 2010
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Neural document summarization by jointly learning to score and select sentences
Qingyu Zhou, Nan Yang, Furu Wei, Shaohan Huang, Ming Zhou, and Tiejun Zhao. 2018 · 2010
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A neural attention model for abstractive sentence summarization
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre, Bing Xiang, et al. 2016 · 2016
Cited alongside, same era.
A sentence compression based framework to query-focused multi-document summarization
Lu Wang, Hema Raghavan, Vittorio Castelli, Radu Florian, and Claire Cardie. 2016 · 2016
Cited alongside, same era.
Searchqa: A new q&a dataset augmented with context from a search engine
Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur Guney, Volkan Cirik, and Kyunghyun Cho. 2017 · 2017
Cited alongside, same era.
Query-based abstractive summarization using neural networks
Johan Hasselqvist, Niklas Helmertz, and Mikael Kågebäck. 2017 · 2017
Cited alongside, same era.
TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Wikihow: A large scale text summarization dataset
Mahnaz Koupaee and William Yang Wang. 2018 · 2018
Later among the works it cites.
Generating wikipedia by summarizing long sequences
Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. 2018 · 2018
Later among the works it cites.
A reinforced topic-aware convolutional sequence-to-sequence model for abstractive text summarization
Li Wang, Junlin Yao, Yunzhe Tao, Li Zhong, Wei Liu, and Qiang Du. 2018 · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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Cross-task knowledge transfer for query-based text summarization
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Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Query-based summarization using MDL principle
Marina Litvak and Natalia Vanetik. 2017 · 2017
Cited alongside, same era.
Diversity driven attention model for query-based abstractive summarization
Preksha Nema, Mitesh M. Khapra, Anirban Laha, and Balaraman Ravindran. 2017 · 2017
Cited alongside, same era.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Cited alongside, same era.
Ms marco: A human generated machine reading comprehension dataset
Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, and Tong Wang. 2018 · 2018
Cited alongside, same era.
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil. 2018 · 2018
Cited alongside, same era.
Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies
Max Grusky, Mor Naaman, and Yoav Artzi. 2018 · 2018
Cited alongside, same era.
Tal Baumel, Matan Eyal, and Michael Elhadad. 2018a
Cited in the paper.
Elozino Egonmwan, Vittorio Castelli, and Md Arafat Sultan. 2019 · 2019
Later among the works it cites.
Multi-news: a large-scale multi-document summarization dataset and abstractive hierarchical model
Alexander R Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir R Radev. 2019 · 2019
Later among the works it cites.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Later among the works it cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019 · 2019
Later among the works it cites.
Self-supervised learning for contextualized extractive summarization
Hong Wang, Xin Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, and William Yang Wang. 2019 · 2019
Later among the works it cites.
Joint learning of answer selection and answer summary generation in community question answering
Yang Deng, Wai Lam, Yuexiang Xie, Daoyuan Chen, Yaliang Li, Min Yang, and Ying Shen. 2020 · 2020
Closest in time.
Neural query-biased abstractive summarization using copying mechanism
Tatsuya Ishigaki, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen, and Manabu Okumura. 2020 · 2020
Closest in time.