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In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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A common theory of information fusion from multiple text sources step one: Cross-document structure
Dragomir Radev. 2000 · 2000
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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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Sentence fusion for multidocument news summarization
Regina Barzilay and Kathleen R. McKeown. 2005 · 2005
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Sentence fusion via dependency graph compression
Katja Filippova and Michael Strube. 2008 · 2008
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The New York Times Annotated Corpus
Evan Sandhaus. 2008 · 2008
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An exploration of document impact on graph-based multi-document summarization
Xiaojun Wan. 2008 · 2008
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Discourse constraints for document compression
James Clarke and Mirella Lapata. 2010 · 2010
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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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Towards coherent multi-document summarization
Janara Christensen, Mausam, Stephen Soderland, and Oren Etzioni. 2013 · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Multi-document summarization using bipartite graphs
Daraksha Parveen and Michael Strube. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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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, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Best-worst scaling: Theory, methods and applications
Jordan J Louviere, Terry N Flynn, and Anthony Alfred John Marley. 2015 · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016 · 2016
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An unsupervised multi-document summarization framework based on neural document model
Shulei Ma, Zhi-Hong Deng, and Yunlun Yang. 2016 · 2016
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Unsupervised neural multi-document abstractive summarization
Eric Chu and Peter J Liu. 2018 · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 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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Taku Kudo and John Richardson. 2018 · 2018
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Automatic detection of vague words and sentences in privacy policies
Logan Lebanoff and Fei Liu. 2018 · 2018
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Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 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
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Structured attention networks
Yoon Kim, Carl Denton, Luong Hoang, and Alexander M Rush. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2017 · 2017
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Best-worst scaling more reliable than rating scales: A case study on sentiment intensity annotation
Svetlana Kiritchenko and Saif Mohammad. 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
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Generating Wikipedia by summarizing long sequences
Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. 2018 · 2018
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Learning structured text representations
Yang Liu and Mirella Lapata. 2018 · 2018
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Ranking sentences for extractive summarization with reinforcement learning
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
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Towards dynamic computation graphs via sparse latent structure
Vlad Niculae, André F. T. Martins, and Claire Cardie. 2018 · 2018
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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
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Do latent tree learning models identify meaningful structure in sentences?
Adina Williams, Andrew Drozdov, and Samuel R. Bowman. 2018 · 2018
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Adapting neural single-document summarization model for abstractive multi-document summarization: A pilot study
Jianmin Zhang, Jiwei Tan, and Xiaojun Wan. 2018 · 2018
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Structured neural summarization
Patrick Fernandes, Miltiadis Allamanis, and Marc Brockschmidt. 2019 · 2019
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