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Neural abstractive summarization models are prone to generate content inconsistent with the source document, i.e.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
R. T. McCoy, E. Pavlick, and T. Linzen. 2019 · 1902
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The curious case of neural text degeneration
A. Holtzman, J. Buys, M. Forbes, and Y. Choi. 2019 · 1904
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BERTSCORE: Evaluating text generation with BERT
T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi. 2019a · 1904
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2019b · 1904
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Question answering is a format; when is it useful?
M. Gardner, J. Berant, H. Hajishirzi, A. Talmor, and S. Min. 2019 · 1909
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Analyzing sentence fusion in abstractive summarization
L. Lebanoff, J. Muchovej, F. Dernoncourt, D. S. Kim, S. Kim, W. Chang, and F. Liu. 2019 · 1910
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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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Don’t say that! making inconsistent dialogue unlikely with unlikelihood training
M. Li, S. Roller, I. Kulikov, S. Welleck, Y. Boureau, K. Cho, and J. Weston. 2019 · 1911
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The TIPSTER SUMMAC text summarization evaluation
I. Mani, G. Klein, L. Hirschman, T. Firmin, D. House, and B. Sundheim. 1999 · 1999
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Summarization beyond sentence extraction: A probabilistic approach to sentence compression
K. Knight and D. Marcu. 2002 · 2002
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Incorporating non-local information into information extraction systems by gibbs sampling
Jenny Rose Finkel, Trond Grenager, and Christopher Manning. 2005 · 2005
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Discourse constraints for document compression
J. Clarke and M. Lapata. 2010 · 2010
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The Stanford CoreNLP natural language processing toolkit
Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven 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, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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A neural network approach to context-sensitive generation of conversational responses
A. Sordoni, M. Galley, M. Auli, C. Brockett, Y. Ji, M. Mitchell, J. Nie, J. Gao, and B. Dolan. 2015 · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
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A diversity-promoting objective function for neural conversation models
J. Li, M. Galley, C. Brockett, J. Gao, and W. B. Dolan. 2016 · 2016
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How NOT to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation
C. Liu, R. Lowe, I. V. Serban, M. Noseworthy, L. Charlin, and J. Pineau. 2016 · 2016
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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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Modeling coverage for neural machine translation
Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016 · 2016
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Enhanced LSTM for natural language inference
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017 · 2017
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Towards an automatic turing test: Learning to evaluate dialogue responses
Neural approaches to conversational AI
Jianfeng Gao, Michel Galley, and Lihong Li. 2018 · 2018
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AllenNLP: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer. 2018 · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018 · 2018
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Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies
M. Grusky, M. Naaman, , and Y. Artzi. 2018 · 2018
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Constituency parsing with a self-attentive encoder
Nikita Kitaev and Dan Klein. 2018 · 2018
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Don’t give me the details, just the summary! Topic-aware convolutional neural networks for extreme summarization
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R. Lowe, M. Noseworthy, I. V. Serban, N. Angelard-Gontier, Y. Bengio, and J. Pineau. 2017 · 2017
Cited alongside, same era.
Why we need new evaluation metrics for NLG
J. Novikova, O. Dušek, A. C. Curry, and V. Rieser. 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.
Neural machine translation with reconstruction
Z. Tu, Y. Liu, L. Shang, X. Liu, and H. Li. 2017 · 2017
Cited alongside, same era.
Challenges in data-to-document generation
S. Wiseman, S. M. Shieber, and A. M. Rush. 2017 · 2017
Cited alongside, same era.
Faithful to the original: Fact aware neural abstractive summarization
Z. Cao, F. Wei, W. Li, and S. Li. 2018 · 2018
Cited alongside, same era.
The price of debiasing automatic metrics in natural language evaluation
A. Chaganty, S. Mussmann, and P. Liang. 2018 · 2018
Cited alongside, same era.
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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On the abstractiveness of neural document summarization
F. Zhang, J. Yao, and R. Yan1. 2018 · 2018
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Language models are unsupervised multitask learners
R. Alec, W. Jeff, C. Rewon, L. David, A. Dario, and S. Ilya. 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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Question answering as an automatic evaluation metric for news article summarization
M. Eyal, T. Baumel, and M. Elhadad. 2019 · 2019
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Assessing the factual accuracy of generated text
B. Goodrich, V. Rao, P. J. Liu, and M. Saleh. 2019 · 2019
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Unifying human and statistical evaluation for natural language generation
T. Hashimoto, H. Zhang, and P. Liang. 2019 · 2019
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Neural text summarization: A critical evaluation
W. Kryściński, N. S. Keskar, B. McCann, C. Xiong, and R. Socher. 2019 · 2019
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Text summarization with pretrained encoders
Y. Liu and M. Lapata. 2019 · 2019
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Answers unite! unsupervised metrics for reinforced summarization models
T. Scialom, S. Lamprier, B. Piwowarski, and J. Staiano. 2019 · 2019
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