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Automatic sentence summarization produces a shorter version of a sentence, while preserving its most important information.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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.
Hedge trimmer: A parse-and-trim approach to headline generation
Bonnie Dorr, David Zajic, and Richard Schwartz. 2003 · 2003
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.
Speech summarization: An approach through word extraction and a method for evaluation
Chiori Hori and Sadaoki Furui. 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.
An introduction to DUC-2004: Intrinsic evaluation of generic news text summarization systems
P Over and J Yen. 2004 · 2004
Earlier work this paper cites.
BBN/UMD at DUC-2004: Topiary
David Zajic, Bonnie Dorr, and Richard Schwartz. 2004 · 2004
Earlier work this paper cites.
Template-filtered headline summarization
Liang Zhou and Eduard Hovy. 2004 · 2004
Earlier work this paper cites.
Constraint-based sentence compression an integer programming approach
James Clarke and Mirella Lapata. 2006 · 2006
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008 · 2008
Earlier work this paper cites.
Exploring content models for multi-document summarization
Aria Haghighi and Lucy Vanderwende. 2009 · 2009
Earlier work this paper cites.
One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson. 2013 · 2013
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
A neural attention model for abstractive sentence summarization
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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.
Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor OK Li. 2016 · 2016
Cited alongside, same era.
Dual learning for machine translation
Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tieyan Liu, and Wei-Ying Ma. 2016 · 2016
Cited alongside, same era.
Siamese CBOW: Optimizing word embeddings for sentence representations
Tom Kenter, Alexey Borisov, and Maarten de Rijke. 2016 · 2016
Cited alongside, same era.
Controlling output length in neural encoder-decoders
Yuta Kikuchi, Graham Neubig, Ryohei Sasano, Hiroya Takamura, and Manabu Okumura. 2016 · 2016
Unsupervised learning of sentence embeddings using compositional n-gram features
Matteo Pagliardini, Prakhar Gupta, and Martin Jaggi. 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.
Learning to encode text as human-readable summaries using generative adversarial networks
Yaushian Wang and Hung-yi Lee. 2018 · 2018
Later among the works it cites.
SEQ 3 : Differentiable sequence-to-sequence-to-sequence autoencoder for unsupervised abstractive sentence compression
Christos Baziotis, Ion Androutsopoulos, Ioannis Konstas, and Alexandros Potamianos. 2019 · 2019
Later among the works it cites.
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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
Cited alongside, same era.
Artificial Intelligence: A Modern Approach
Stuart J Russell and Peter Norvig. 2016 · 2016
Cited alongside, same era.
Online segment to segment neural transduction
Lei Yu, Jan Buys, and Phil Blunsom. 2016 · 2016
Cited alongside, same era.
Guided open vocabulary image captioning with constrained beam search
Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2017 · 2017
Cited alongside, same era.
Lexically constrained decoding for sequence generation using grid beam search
Chris Hokamp and Qun Liu. 2017 · 2017
Cited alongside, same era.
Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017 · 2017
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
EditNTS: An neural programmer-interpreter model for sentence simplification through explicit editing
Yue Dong, Zichao Li, Mehdi Rezagholizadeh, and Jackie Chi Kit Cheung. 2019 · 2019
Later among the works it cites.
Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model
Alexander R Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir R Radev. 2019 · 2019
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CGMH: Constrained sentence generation by Metropolis-Hastings sampling
Ning Miao, Hao Zhou, Lili Mou, Rui Yan, and Lei Li. 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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How to compare summarizers without target length? Pitfalls, solutions and re-examination of the neural summarization literature
Simeng Sun, Ori Shapira, Ido Dagan, and Ani Nenkova. 2019 · 2019
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BiSET: Bi-directional selective encoding with template for abstractive summarization
Kai Wang, Xiaojun Quan, and Rui Wang. 2019 · 2019
Later among the works it cites.
BottleSum: Unsupervised and self-supervised sentence summarization using the information bottleneck principle
Peter West, Ari Holtzman, Jan Buys, and Yejin Choi. 2019 · 2019
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Sentence centrality revisited for unsupervised summarization
Hao Zheng and Mirella Lapata. 2019 · 2019
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Simple unsupervised summarization by contextual matching
Jiawei Zhou and Alexander M Rush. 2019 · 2019
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Iterative edit-based unsupervised sentence simplification
Dhruv Kumar, Lili Mou, Lukasz Golab, and Olga Vechtomova. 2020 · 2020
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Unsupervised paraphrasing by simulated annealing
Xianggen Liu, Lili Mou, Fandong Meng, Hao Zhou, Jie Zhou, and Sen Song. 2020 · 2020
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