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Sequence-to-sequence models for abstractive summarization have been studied extensively, yet the generated summaries commonly suffer from fabricated content, and are often found to be near-extractive.
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
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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The automatic creation of literature abstracts
Hans Peter Luhn. 1958 · 1958
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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 new york times annotated corpus
Evan Sandhaus. 2008 · 2008
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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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The Stanford CoreNLP natural language processing toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014 · 2014
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Leveraging linguistic structure for open domain information extraction
Gabor Angeli, Melvin Jose Johnson Premkumar, and Christopher D. Manning. 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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2015 · 2015
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2016 · 2016
Cited alongside, same era.
Why we need new evaluation metrics for nlg
Jekaterina Novikova, Ondrej Dusek, Amanda Cercas Curry, and Verena Rieser. 2017 · 2017
Cited alongside, same era.
Self-critical sequence training for image captioning
Steven J Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel. 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.
Abstractive document summarization with a graph-based attentional neural model
Jiwei Tan, Xiaojun Wan, and Jianguo Xiao. 2017 · 2017
Cited alongside, same era.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
Later among the works it cites.
A graph-to-sequence model for AMR-to-text generation
Linfeng Song, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2018 · 2018
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Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
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Guiding extractive summarization with question-answering rewards
Kristjan Arumae and Fei Liu. 2019 · 2019
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Structural neural encoders for amr-to-text generation
Marco Damonte and Shay B Cohen. 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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Daniel Beck, Gholamreza Haffari, and Trevor Cohn. 2018 · 2018
Cited alongside, same era.
Faithful to the original: Fact aware neural abstractive summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018 · 2018
Cited alongside, same era.
Deep communicating agents for abstractive summarization
Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi. 2018 · 2018
Cited alongside, same era.
Fast abstractive summarization with reinforce-selected sentence rewriting
Yen-Chun Chen and Mohit Bansal. 2018 · 2018
Cited alongside, same era.
Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018 · 2018
Cited alongside, same era.
A unified model for extractive and abstractive summarization using inconsistency loss
Wan-Ting Hsu, Chieh-Kai Lin, Ming-Ying Lee, Kerui Min, Jing Tang, and Min Sun. 2018 · 2018
Cited alongside, same era.
Improving abstraction in text summarization
Wojciech Kryściński, Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
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
Later among the works it cites.
Question answering as an automatic evaluation metric for news article summarization
Matan Eyal, Tal Baumel, and Michael Elhadad. 2019 · 2019
Later among the works it cites.
Using local knowledge graph construction to scale Seq2Seq models to multi-document inputs
Angela Fan, Claire Gardent, Chloé Braud, and Antoine Bordes. 2019 · 2019
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Structured neural summarization
Patrick Fernandes, Miltiadis Allamanis, and Marc Brockschmidt. 2019 · 2019
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Text Generation from Knowledge Graphs with Graph Transformers
Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
Later among the works it cites.
Answers unite! unsupervised metrics for reinforced summarization models
Thomas Scialom, Sylvain Lamprier, Benjamin Piwowarski, and Jacopo Staiano. 2019 · 2019
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An entity-driven framework for abstractive summarization
Eva Sharma, Luyang Huang, Zhe Hu, and Lu Wang. 2019 · 2019
Later among the works it cites.