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Abstractive summarization systems today produce fluent and relevant output, but often "hallucinate" statements not supported by the source text.
Sticking to the facts: Confident decoding for faithful data-to-text generation
Ran Tian, Shashi Narayan, Thibault Sellam, and Ankur P. Parikh. 2019 · 1910
Earlier work this paper cites.
On faithfulness and factuality in abstractive summarization
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Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
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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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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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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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Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre, Bing Xiang, et al. 2016 · 2016
Earlier work this paper cites.
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Earlier work this paper cites.
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I. Loshchilov and F. Hutter. 2017 · 2017
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Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
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A discourse-aware attention model for abstractive summarization of long documents
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Cited alongside, same era.
FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
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spaCy: Industrial-strength Natural Language Processing in Python
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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Tobias Falke, Leonardo F. R. Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019 · 2019
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Eva Sharma, Chen Li, and Lu Wang. 2019 · 2019
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Can unconditional language models recover arbitrary sentences?
Nishant Subramani, Samuel R. Bowman, and Kyunghyun Cho. 2019 · 2019
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