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Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure.
Discourse strategies for generating natural-language text
Kathleen R McKeown. 1985 · 1985
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Centering: A framework for modeling the local coherence of discourse
Barbara J. Grosz, Scott Weinstein, and Aravind K. Joshi. 1995 · 1995
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Functional centering grounding referential coherence in information structure
Michael Strube and Udo Hahn. 1999 · 1999
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Nltk: The natural language toolkit
Edward Loper and Steven Bird. 2002 · 2002
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Automatic evaluation of summaries using n-gram co-occurrence statistics
Chin-Yew Lin and Eduard Hovy. 2003 · 2003
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Catching the drift: Probabilistic content models, with applications to generation and summarization
Regina Barzilay and Lillian Lee. 2004 · 2004
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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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Textrank: Bringing order into text
Rada Mihalcea and Paul Tarau. 2004 · 2004
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The impact of frequency on summarization
Ani Nenkova and Lucy Vanderwende. 2005 · 2005
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Modeling local coherence: An entity-based approach
Regina Barzilay and Mirella Lapata. 2008 · 2008
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The new york times annotated corpus
Evan Sandhaus. 2008 · 2008
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Natural language processing with Python: analyzing text with the natural language toolkit
Steven Bird, Ewan Klein, and Edward Loper. 2009 · 2009
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Exploring content models for multi-document summarization
Aria Haghighi and Lucy Vanderwende. 2009 · 2009
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer. 2011 · 2011
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Annotated gigaword
Courtney Napoles, Matthew Gormley, and Benjamin Van Durme. 2012 · 2012
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Patent summarization and paraphrasing
David Cinciruk. 2015 · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Abstractive document summarization with a graph-based attentional neural model
Jiwei Tan, Xiaojun Wan, and Jianguo Xiao. 2017 · 2017
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Detecting (un)important content for single-document news summarization
Yinfei Yang, Forrest Bao, and Ani Nenkova. 2017 · 2017
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Faithful to the original: Fact aware neural abstractive summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018 · 2018
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Fast abstractive summarization with reinforce-selected sentence rewriting
Yen-Chun Chen and Mohit Bansal. 2018 · 2018
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A discourse-aware attention model for abstractive summarization of long documents
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018 · 2018
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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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Variations of the similarity function of textrank for automated summarization
Federico Barrios, Federico López, Luis Argerich, and Rosa Wachenchauzer. 2016 · 2016
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016 · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
Cited alongside, same era.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 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.
Lisa Fan, Dong Yu, and Lu Wang. 2018 · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018a · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018b · 2018
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Google patents public datasets: connecting public, paid, and private patent data
Google. 2018 · 2018
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Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies
Max Grusky, Mor Naaman, and Yoav Artzi. 2018 · 2018
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
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Cooperative patent classification scheme
USPTO. 2013 · 2018
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