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Recent neural network approaches to summarization are largely either selection-based extraction or generation-based abstraction.
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.
Statistics-Based Summarization - Step One: Sentence Compression
Kevin Knight and Daniel Marcu. 2000 · 2000
Earlier work this paper cites.
Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory
Lynn Carlson, Daniel Marcu, and Mary Ellen Okurovsky. 2001 · 2001
Earlier work this paper cites.
Summarization Beyond Sentence Extraction: A Probabilistic Approach to Sentence Compression
Kevin Knight and Daniel Marcu. 2002 · 2002
Earlier work this paper cites.
ROUGE: A Package for Automatic Evaluation of Summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints
Greg Durrett, Taylor Berg-Kirkpatrick, and Dan Klein. 2016 · 2008
Earlier work this paper cites.
The New York Times Annotated Corpus
Evan Sandhaus. 2008 · 2008
Earlier work this paper cites.
Sentence Compression As Tree Transduction
Trevor Cohn and Mirella Lapata. 2009 · 2009
Earlier work this paper cites.
A Scalable Global Model for Summarization
Dan Gillick and Benoit Favre. 2009 · 2009
Earlier work this paper cites.
Summarization with a joint model for sentence extraction and compression
Andre Martins and Noah A. Smith. 2009 · 2009
Earlier work this paper cites.
Jointly Learning to Extract and Compress
Taylor Berg-Kirkpatrick, Dan Gillick, and Dan Klein. 2011 · 2011
Earlier work this paper cites.
Learning to Simplify Sentences with Quasi-Synchronous Grammar and Integer Programming
Kristian Woodsend and Mirella Lapata. 2011 · 2011
Earlier work this paper cites.
A Dynamic Oracle for Arc-Eager Dependency Parsing
Yoav Goldberg and Joakim Nivre. 2012 · 2012
Earlier work this paper cites.
Single-Document Summarization as a Tree Knapsack Problem
Tsutomu Hirao, Yasuhisa Yoshida, Masaaki Nishino, Norihito Yasuda, and Masaaki Nagata. 2013 · 2013
Earlier work this paper cites.
Fast Joint Compression and Summarization via Graph Cuts
Xian Qian and Yang Liu. 2013 · 2013
Earlier work this paper cites.
A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization
Lu Wang, Hema Raghavan, Vittorio Castelli, Radu Florian, and Claire Cardie. 2013 · 2013
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees
Chen Li, Yang Liu, Fei Liu, Lin Zhao, and Fuliang Weng. 2014 · 2014
Cited alongside, same era.
The Stanford CoreNLP Natural Language Processing Toolkit
Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014 · 2014
Cited alongside, same era.
Sentence Compression by Deletion with LSTMs
Katja Filippova, Enrique Alfonseca, Carlos A. Colmenares, Lukasz Kaiser, and Oriol Vinyals. 2015 · 2015
Cited alongside, same era.
Can syntax help? improving an LSTM-based sentence compression model for new domains
Liangguo Wang, Jing Jiang, Hai Leong Chieu, Chen Hui Ong, Dandan Song, and Lejian Liao. 2017 · 2017
Later among the works it cites.
Faithful to the Original: Fact Aware Neural Abstractive Summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018 · 2018
Later among the works it cites.
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
Yen-Chun Chen and Mohit Bansal. 2018 · 2018
Later among the works it cites.
BanditSum: Extractive Summarization as a Contextual Bandit
Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, and Jackie Chi Kit Cheung. 2018 · 2018
Later among the works it cites.
Bottom-Up Abstractive Summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018 · 2018
Later among the works it cites.
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies
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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
Cited alongside, same era.
A Neural Attention Model for Abstractive Sentence Summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Cited alongside, same era.
Neural Summarization by Extracting Sentences and Words
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
Cited alongside, same era.
Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
Sumit Chopra, Michael Auli, and Alexander M. Rush. 2016 · 2016
Cited alongside, same era.
The role of discourse units in near-extractive summarization
Junyi Jessy Li, Kapil Thadani, and Amanda Stent. 2016 · 2016
Cited alongside, same era.
Language as a Latent Variable: Discrete Generative Models for Sentence Compression
Yishu Miao and Phil Blunsom. 2016 · 2016
Cited alongside, same era.
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016 · 2016
Cited alongside, same era.
Max Grusky, Mor Naaman, and Yoav Artzi. 2018 · 2018
Later among the works it cites.
Closed-Book Training to Improve Summarization Encoder Memory
Yichen Jiang and Mohit Bansal. 2018 · 2018
Later among the works it cites.
Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization
Haoran Li, Junnan Zhu, Jiajun Zhang, and Chengqing Zong. 2018 · 2018
Later among the works it cites.
Ranking sentences for extractive summarization with reinforcement learning
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Later among the works it cites.
A Deep Reinforced Model for Abstractive Summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
Later among the works it cites.
Deep Contextualized Word Representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
Neural Latent Extractive Document Summarization
Xingxing Zhang, Mirella Lapata, Furu Wei, and Ming Zhou. 2018 · 2018
Later among the works it cites.
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 · 2018
Later among the works it cites.
Chameleon: a scalable production testbed for computer science research
Kate Keahey, Pierre Riteau, Dan Stanzione, Tim Cockerill, Joe Mambretti, Paul Rad, and Paul Ruth. 2019 · 2019
Closest in time.
Jointly Extracting and Compressing Documents with Summary State Representations
Afonso Mendes, Shashi Narayan, Sebastião Miranda, Zita Marinho, André F. T. Martins, and Shay B. Cohen. 2019 · 2019
Closest in time.