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Unsupervised extractive document summarization aims to select important sentences from a document without using labeled summaries during training.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
Earlier work this paper cites.
Topical coherence for graph-based extractive summarization
Daraksha Parveen, Hans-Martin Ramsl, and Michael Strube. 2015 · 1954
Earlier work this paper cites.
A trainable document summarizer
Julian Kupiec, Jan Pedersen, and Francine Chen. 1995 · 1995
Earlier work this paper cites.
Pagerank: Bringing order to the web
Larry Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1997 · 1997
Earlier work this paper cites.
The use of mmr, diversity-based reranking for reordering documents and producing summaries
Jaime Carbonell and Jade Goldstein. 1998 · 1998
Earlier work this paper cites.
The pagerank citation ranking: Bringing order to the web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999 · 1999
Earlier work this paper cites.
Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies
Dragomir R. Radev, Hongyan Jing, and Malgorzata Budzikowska. 2000 · 2000
Earlier work this paper cites.
Text summarization via hidden markov models
John M Conroy and Dianne P O’leary. 2001 · 2001
Earlier work this paper cites.
Ted: A pretrained unsupervised summarization model with theme modeling and denoising
Zi-Yi Yang, Chenguang Zhu, Robert Gmyr, Michael Zeng, and Xuedong Huang. 2020 · 2001
Earlier work this paper cites.
From single to multi-document summarization
Chin-Yew Lin and Eduard Hovy. 2002 · 2002
Earlier work this paper cites.
Lexrank: Graph-based lexical centrality as salience in text summarization
Günes Erkan and Dragomir R Radev. 2004 · 2004
Earlier work this paper cites.
Event-based extractive summarization
Elena Filatova and Vasileios Hatzivassiloglou. 2004 · 2004
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Textrank: Bringing order into text
Rada Mihalcea and Paul Tarau. 2004 · 2004
Earlier work this paper cites.
Mead-a platform for multidocument multilingual text summarization.(2004)
Dragomir R Radev, Timothy Allison, Sasha Blair-Goldensohn, John Blitzer, Arda Celebi, Stanko Dimitrov, Elliott Drabek, Ali Hakim, Wai Lam, Danyu Liu, et al. 2004 · 2004
Earlier work this paper cites.
Pretraining with contrastive sentence objectives improves discourse performance of language models
Dan Iter, Kelvin Guu, Larry Lansing, and Dan Jurafsky. 2020 · 2005
Earlier work this paper cites.
A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization
Ani Nenkova, Lucy Vanderwende, and Kathleen McKeown. 2006 · 2006
Earlier work this paper cites.
The new york times annotated corpus
Evan Sandhaus. 2008 · 2008
Earlier work this paper cites.
An exploration of document impact on graph-based multi-document summarization
Xiaojun Wan. 2008 · 2008
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Multi-document summarization using cluster-based link analysis
Xiaojun Wan and Jianwu Yang. 2008 · 2008
Cited alongside, same era.
Automatic summarization
Ani Nenkova and Kathleen McKeown. 2011 · 2011
Cited alongside, same era.
Single-document summarization as a tree knapsack problem
Tsutomu Hirao, Yasuhisa Yoshida, Masaaki Nishino, Norihito Yasuda, and Masaaki Nagata. 2013 · 2013
Cited alongside, same era.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013 · 2013
Cited alongside, same era.
The Stanford CoreNLP natural language processing toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014 · 2014
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
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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
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Unsupervised sentence compression using denoising auto-encoders
Thibault Fevry and Jason Phang. 2018 · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 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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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
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Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
Semi-supervised sequence learning
Andrew M Dai and Quoc V Le. 2015 · 2015
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.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Learning to encode text as human-readable summaries using generative adversarial networks
Yaushian Wang and Hung-Yi Lee. 2018 · 2018
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Neural latent extractive document summarization
Xingxing Zhang, Mirella Lapata, Furu Wei, and Ming Zhou. 2018 · 2018
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SEQˆ3: Differentiable sequence-to-sequence-to-sequence autoencoder for unsupervised abstractive sentence compression
Christos Baziotis, Ion Androutsopoulos, Ioannis Konstas, and Alexandros Potamianos. 2019 · 2019
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Meansum: A neural model for unsupervised multi-document abstractive summarization
Eric Chu and Peter J. Liu. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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HIBERT: Document level pre-training of hierarchical bidirectional transformers for document summarization
Xingxing Zhang, Furu Wei, and Ming Zhou. 2019 · 2019
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Sentence centrality revisited for unsupervised summarization
Hao Zheng and Mirella Lapata. 2019 · 2019
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