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    <titleInfo>
        <title>Learning to Score System Summaries for Better Content Selection Evaluation.</title>
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    <name type="personal">
        <namePart type="given">Maxime</namePart>
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    <name type="personal">
        <namePart type="given">Teresa</namePart>
        <namePart type="family">Botschen</namePart>
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            <title>Proceedings of the Workshop on New Frontiers in Summarization</title>
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        <name type="personal">
            <namePart type="given">Lu</namePart>
            <namePart type="family">Wang</namePart>
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        <name type="personal">
            <namePart type="given">Jackie</namePart>
            <namePart type="given">Chi</namePart>
            <namePart type="given">Kit</namePart>
            <namePart type="family">Cheung</namePart>
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        <name type="personal">
            <namePart type="given">Giuseppe</namePart>
            <namePart type="family">Carenini</namePart>
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            <namePart type="given">Fei</namePart>
            <namePart type="family">Liu</namePart>
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    <abstract>The evaluation of summaries is a challenging but crucial task of the summarization field. In this work, we propose to learn an automatic scoring metric based on the human judgements available as part of classical summarization datasets like TAC-2008 and TAC-2009. Any existing automatic scoring metrics can be included as features, the model learns the combination exhibiting the best correlation with human judgments. The reliability of the new metric is tested in a further manual evaluation where we ask humans to evaluate summaries covering the whole scoring spectrum of the metric. We release the trained metric as an open-source tool.</abstract>
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    <identifier type="doi">10.18653/v1/W17-4510</identifier>
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        <date>2017-09</date>
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%0 Conference Proceedings
%T Learning to Score System Summaries for Better Content Selection Evaluation.
%A Peyrard, Maxime
%A Botschen, Teresa
%A Gurevych, Iryna
%Y Wang, Lu
%Y Cheung, Jackie Chi Kit
%Y Carenini, Giuseppe
%Y Liu, Fei
%S Proceedings of the Workshop on New Frontiers in Summarization
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F peyrard-etal-2017-learning
%X The evaluation of summaries is a challenging but crucial task of the summarization field. In this work, we propose to learn an automatic scoring metric based on the human judgements available as part of classical summarization datasets like TAC-2008 and TAC-2009. Any existing automatic scoring metrics can be included as features, the model learns the combination exhibiting the best correlation with human judgments. The reliability of the new metric is tested in a further manual evaluation where we ask humans to evaluate summaries covering the whole scoring spectrum of the metric. We release the trained metric as an open-source tool.
%R 10.18653/v1/W17-4510
%U https://aclanthology.org/W17-4510
%U https://doi.org/10.18653/v1/W17-4510
%P 74-84

Markdown (Informal)

[Learning to Score System Summaries for Better Content Selection Evaluation.](https://aclanthology.org/W17-4510) (Peyrard et al., 2017)

• Learning to Score System Summaries for Better Content Selection Evaluation. (Peyrard et al., 2017)

ACL

• Maxime Peyrard, Teresa Botschen, and Iryna Gurevych. 2017. Learning to Score System Summaries for Better Content Selection Evaluation.. In Proceedings of the Workshop on New Frontiers in Summarization, pages 74–84, Copenhagen, Denmark. Association for Computational Linguistics.