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Reinforcement Learning (RL) based document summarisation systems yield state-of-the-art performance in terms of ROUGE scores, because they directly use ROUGE as the rewards during training.
Algorithms for inverse reinforcement learning
Andrew Y. Ng and Stuart J. Russell. 2000 · 2000
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BLEU: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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The meteor metric for automatic evaluation of machine translation
Alon Lavie and Michael J. Denkowski. 2009 · 2009
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Framework of automatic text summarization using reinforcement learning
Seonggi Ryang and Takeshi Abekawa. 2012 · 2012
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Automatically assessing machine summary content without a gold standard
Annie Louis and Ani Nenkova. 2013 · 2013
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Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Fear the REAPER: A system for automatic multi-document summarization with reinforcement learning
Cody Rioux, Sadid A. Hasan, and Yllias Chali. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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SQuAD: 100, 000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
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Bandit structured prediction for neural sequence-to-sequence learning
Julia Kreutzer, Artem Sokolov, and Stefan Riezler. 2017 · 2017
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Why we need new evaluation metrics for NLG
Jekaterina Novikova, Ondrej Dusek, Amanda Cercas Curry, and Verena Rieser. 2017 · 2017
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Learning to score system summaries for better content selection evaluation
Maxime Peyrard, Teresa Botschen, and Iryna Gurevych. 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
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The price of debiasing automatic metrics in natural language evalaution
Arun Chaganty, Stephen Mussmann, and Percy Liang. 2018 · 2018
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Fast abstractive summarization with reinforce-selected sentence rewriting
Yen-Chun Chen and Mohit Bansal. 2018 · 2018
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Banditsum: Extractive summarization as a contextual bandit
Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, and Jackie Chi Kit Cheung. 2018 · 2018
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
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Objective function learning to match human judgements for optimization-based summarization
Maxime Peyrard and Iryna Gurevych. 2018 · 2018
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Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations
Andreas Rücklé, Steffen Eger, Maxime Peyrard, and Iryna Gurevych. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 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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APRIL: interactively learning to summarise by combining active preference learning and reinforcement learning
Yang Gao, Christian M. Meyer, and Iryna Gurevych. 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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Content selection in deep learning models of summarization
Chris Kedzie, Kathleen R. McKeown, and Hal Daumé III. 2018 · 2018
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Reliability and learnability of human bandit feedback for sequence-to-sequence reinforcement learning
Julia Kreutzer, Joshua Uyheng, and Stefan Riezler. 2018 · 2018
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Improving abstraction in text summarization
Wojciech Kryscinski, Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
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Ranking sentences for extractive summarization with reinforcement learning
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018b · 2018
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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
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A BERT Baseline for the Natural Questions
Chris Alberti, Kenton Lee, and Michael Collins. 2019 · 2019
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Guiding extractive summarization with question-answering rewards
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Yang Gao, Christian M. Meyer, and Iryna Gurevych. 2019 · 2019
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Reward learning for efficient reinforcement learning in extractive document summarisation
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How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature
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Document modeling with external attention for sentence extraction
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