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        <title>Fact-based Content Weighting for Evaluating Abstractive Summarisation</title>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>Abstractive summarisation is notoriously hard to evaluate since standard word-overlap-based metrics are insufficient. We introduce a new evaluation metric which is based on fact-level content weighting, i.e. relating the facts of the document to the facts of the summary. We fol- low the assumption that a good summary will reflect all relevant facts, i.e. the ones present in the ground truth (human-generated refer- ence summary). We confirm this hypothe- sis by showing that our weightings are highly correlated to human perception and compare favourably to the recent manual highlight- based metric of Hardy et al. (2019).</abstract>
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        <date>2020-07</date>
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%0 Conference Proceedings
%T Fact-based Content Weighting for Evaluating Abstractive Summarisation
%A Xu, Xinnuo
%A Dušek, Ondřej
%A Li, Jingyi
%A Rieser, Verena
%A Konstas, Ioannis
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F xu-etal-2020-fact
%X Abstractive summarisation is notoriously hard to evaluate since standard word-overlap-based metrics are insufficient. We introduce a new evaluation metric which is based on fact-level content weighting, i.e. relating the facts of the document to the facts of the summary. We fol- low the assumption that a good summary will reflect all relevant facts, i.e. the ones present in the ground truth (human-generated refer- ence summary). We confirm this hypothe- sis by showing that our weightings are highly correlated to human perception and compare favourably to the recent manual highlight- based metric of Hardy et al. (2019).
%R 10.18653/v1/2020.acl-main.455
%U https://aclanthology.org/2020.acl-main.455
%U https://doi.org/10.18653/v1/2020.acl-main.455
%P 5071-5081

Markdown (Informal)

[Fact-based Content Weighting for Evaluating Abstractive Summarisation](https://aclanthology.org/2020.acl-main.455) (Xu et al., ACL 2020)

• Fact-based Content Weighting for Evaluating Abstractive Summarisation (Xu et al., ACL 2020)

ACL

• Xinnuo Xu, Ondřej Dušek, Jingyi Li, Verena Rieser, and Ioannis Konstas. 2020. Fact-based Content Weighting for Evaluating Abstractive Summarisation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5071–5081, Online. Association for Computational Linguistics.