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        <title>Reducing Quantity Hallucinations in Abstractive Summarization</title>
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    <abstract>It is well-known that abstractive summaries are subject to hallucination—including material that is not supported by the original text. While summaries can be made hallucination-free by limiting them to general phrases, such summaries would fail to be very informative. Alternatively, one can try to avoid hallucinations by verifying that any specific entities in the summary appear in the original text in a similar context. This is the approach taken by our system, Herman. The system learns to recognize and verify quantity entities (dates, numbers, sums of money, etc.) in a beam-worth of abstractive summaries produced by state-of-the-art models, in order to up-rank those summaries whose quantity terms are supported by the original text. Experimental results demonstrate that the ROUGE scores of such up-ranked summaries have a higher Precision than summaries that have not been up-ranked, without a comparable loss in Recall, resulting in higher F1. Preliminary human evaluation of up-ranked vs. original summaries shows people’s preference for the former.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.203</identifier>
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%0 Conference Proceedings
%T Reducing Quantity Hallucinations in Abstractive Summarization
%A Zhao, Zheng
%A Cohen, Shay B.
%A Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhao-etal-2020-reducing
%X It is well-known that abstractive summaries are subject to hallucination—including material that is not supported by the original text. While summaries can be made hallucination-free by limiting them to general phrases, such summaries would fail to be very informative. Alternatively, one can try to avoid hallucinations by verifying that any specific entities in the summary appear in the original text in a similar context. This is the approach taken by our system, Herman. The system learns to recognize and verify quantity entities (dates, numbers, sums of money, etc.) in a beam-worth of abstractive summaries produced by state-of-the-art models, in order to up-rank those summaries whose quantity terms are supported by the original text. Experimental results demonstrate that the ROUGE scores of such up-ranked summaries have a higher Precision than summaries that have not been up-ranked, without a comparable loss in Recall, resulting in higher F1. Preliminary human evaluation of up-ranked vs. original summaries shows people’s preference for the former.
%R 10.18653/v1/2020.findings-emnlp.203
%U https://aclanthology.org/2020.findings-emnlp.203
%U https://doi.org/10.18653/v1/2020.findings-emnlp.203
%P 2237-2249

Markdown (Informal)

[Reducing Quantity Hallucinations in Abstractive Summarization](https://aclanthology.org/2020.findings-emnlp.203) (Zhao et al., Findings 2020)

• Reducing Quantity Hallucinations in Abstractive Summarization (Zhao et al., Findings 2020)

ACL

• Zheng Zhao, Shay B. Cohen, and Bonnie Webber. 2020. Reducing Quantity Hallucinations in Abstractive Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2237–2249, Online. Association for Computational Linguistics.