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A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2019 · 1910
Earlier work this paper cites.
Don’t say that! making inconsistent dialogue unlikely with unlikelihood training
Margaret Li, Stephen Roller, Ilia Kulikov, Sean Welleck, Y-Lan Boureau, Kyunghyun Cho, and Jason Weston. 2019 · 1911
Earlier work this paper cites.
Measuring nominal scale agreement among many raters
J.L. Fleiss et al. 1971 · 1971
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
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Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, and Meng Jiang. 2020 · 2003
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
A neural attention model for abstractive sentence summarization
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Earlier work this paper cites.
Abstractive text summarization using sequence-to-sequence RNNs and beyond
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Faithful to the original: Fact aware neural abstractive summarization
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Later among the works it cites.
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Later among the works it cites.
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Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2019 · 2019
Later among the works it cites.
Feqa: A question answering evaluation framework for faithfulness assessment in abstractive summarization
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Sebastian Gehrmann, Zachary Ziegler, and Alexander Rush. 2019 · 2019
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A good sample is hard to find: Noise injection sampling and self-training for neural language generation models
Chris Kedzie and Kathleen McKeown. 2019 · 2019
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Evaluating the factual consistency of abstractive text summarization
Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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Neural text summarization: A critical evaluation
Wojciech Kryscinski, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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End-to-end synthetic data generation for domain adaptation of question answering systems
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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2020
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Improving faithfulness in abstractive summarization with contrast candidate generation and selection
Sihao Chen, Fan Zhang, Kazoo Sone, and Dan Roth. 2021 · 2021
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Entity-level factual consistency of abstractive text summarization
Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, and Bing Xiang. 2021 · 2021
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