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Automatic text summarization has experienced substantial progress in recent years.
A general framework for information extraction using dynamic span graphs
Yi Luan, Dave Wadden, Luheng He, Amy Shah, Mari Ostendorf, and Hannaneh Hajishirzi. 2019 · 1904
Earlier work this paper cites.
Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 1908
Earlier work this paper cites.
Entity, relation, and event extraction with contextualized span representations
David Wadden, Ulme Wennberg, Yi Luan, and Hannaneh Hajishirzi. 2019 · 1909
Earlier work this paper 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 · 1910
Earlier work this paper cites.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
Earlier work this paper cites.
Psychological processes as linguistic explanation
Herbert H Clark and Susan E Haviland. 1974 · 1974
Earlier work this paper cites.
What’s new? acquiring new information as a process in comprehension
Susan E Haviland and Herbert H Clark. 1974 · 1974
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Discourse production and comprehension
Herbert H Clark, S Haviland, and Roy O Freedle. 1977 · 1977
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Toward a model of text comprehension and production
Walter Kintsch and Teun A Van Dijk. 1978 · 1978
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Automatic summarizing: factors and directions
Karen Spärck Jones. 1998 · 1998
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Automatic summarization , volume 3
Inderjeet Mani. 2001 · 2001
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The IIR evaluation model: a framework for evaluation of interactive information retrieval systems
Pia Borlund. 2003 · 2003
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2004
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Esin Durmus, He He, and Mona Diab. 2020 · 2005
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Psychology of language, Fifth Edition
David W Carroll. 2008 · 2008
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Who, what, when, where, why? comparing multiple approaches to the cross-lingual 5w task
Kristen Parton, Kathleen McKeown, Robert Eric Coyne, Mona T Diab, Ralph Grishman, Dilek Hakkani-Tür, Mary Harper, Heng Ji, Wei Yun Ma, Adam Meyers, et al. 2009 · 2009
Cited alongside, same era.
Extracting summary knowledge graphs from long documents
Zeqiu Wu, Rik Koncel-Kedziorski, Mari Ostendorf, and Hannaneh Hajishirzi. 2020 · 2009
Cited alongside, same era.
What makes a good summary? Reconsidering the focus of automatic summarization
Maartje ter Hoeve, Julia Kiseleva, and Maarten de Rijke. 2020 · 2012
Cited alongside, same era.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Cited alongside, same era.
Factual error correction for abstractive summarization models
Meng Cao, Yue Dong, Jiapeng Wu, and Jackie Chi Kit Cheung. 2020 · 2020
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SpanBERT: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Extractive opinion summarization in quantized transformer spaces
Stefanos Angelidis, Reinald Kim Amplayo, Yoshihiko Suhara, Xiaolan Wang, and Mirella Lapata. 2021 · 2021
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Jianpeng Cheng and Mirella Lapata. 2016 · 2016
Cited alongside, same era.
Bringing structure into summaries: Crowdsourcing a benchmark corpus of concept maps
Tobias Falke and Iryna Gurevych. 2017 · 2017
Cited alongside, same era.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Snorkel: Rapid training data creation with weak supervision
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 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.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018 · 2018
Cited alongside, same era.
Constituency parsing with a self-attentive encoder
Nikita Kitaev and Dan Klein. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
Learning opinion summarizers by selecting informative reviews
Arthur Bražinskas, Mirella Lapata, and Ivan Titov. 2021 · 2021
Later among the works it cites.
Unsupervised extractive summarization by human memory simulation
Ronald Cardenas, Matthias Galle, and Shay B Cohen. 2021 · 2021
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TWEETSUMM - a dialog summarization dataset for customer service
Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, and Ranit Aharonov. 2021 · 2021
Later among the works it cites.
Leveraging information bottleneck for scientific document summarization
Jiaxin Ju, Ming Liu, Huan Yee Koh, Yuan Jin, Lan Du, and Shirui Pan. 2021 · 2021
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Timeline summarization based on event graph compression via time-aware optimal transport
Manling Li, Tengfei Ma, Mo Yu, Lingfei Wu, Tian Gao, Heng Ji, and Kathleen McKeown. 2021 · 2021
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Topic-aware contrastive learning for abstractive dialogue summarization
Junpeng Liu, Yanyan Zou, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Caixia Yuan, and Xiaojie Wang. 2021 · 2021
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Bringing structure into summaries: a faceted summarization dataset for long scientific documents
Rui Meng, Khushboo Thaker, Lei Zhang, Yue Dong, Xingdi Yuan, Tong Wang, and Daqing He. 2021 · 2021
Later among the works it cites.
mT5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021 · 2021
Later among the works it cites.
Multi-TimeLine summarization (MTLS): Improving timeline summarization by generating multiple summaries
Yi Yu, Adam Jatowt, Antoine Doucet, Kazunari Sugiyama, and Masatoshi Yoshikawa. 2021 · 2021
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Factgraph: Evaluating factuality in summarization with semantic graph representations
Leonardo F. R. Ribeiro, Mengwen Liu, Iryna Gurevych, Markus Dreyer, and Mohit Bansal. 2022 · 2022
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