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        <title>Automated Pyramid Summarization Evaluation</title>
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    <name type="personal">
        <namePart type="given">Yanjun</namePart>
        <namePart type="family">Gao</namePart>
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    <name type="personal">
        <namePart type="given">Chen</namePart>
        <namePart type="family">Sun</namePart>
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    <name type="personal">
        <namePart type="given">Rebecca</namePart>
        <namePart type="given">J</namePart>
        <namePart type="family">Passonneau</namePart>
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        <dateIssued>2019-11</dateIssued>
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Aline</namePart>
            <namePart type="family">Villavicencio</namePart>
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    <abstract>Pyramid evaluation was developed to assess the content of paragraph length summaries of source texts. A pyramid lists the distinct units of content found in several reference summaries, weights content units by how many reference summaries they occur in, and produces three scores based on the weighted content of new summaries. We present an automated method that is more efficient, more transparent, and more complete than previous automated pyramid methods. It is tested on a new dataset of student summaries, and historical NIST data from extractive summarizers.</abstract>
    <identifier type="citekey">gao-etal-2019-automated</identifier>
    <identifier type="doi">10.18653/v1/K19-1038</identifier>
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        <url>https://aclanthology.org/K19-1038</url>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Automated Pyramid Summarization Evaluation
%A Gao, Yanjun
%A Sun, Chen
%A Passonneau, Rebecca J.
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F gao-etal-2019-automated
%X Pyramid evaluation was developed to assess the content of paragraph length summaries of source texts. A pyramid lists the distinct units of content found in several reference summaries, weights content units by how many reference summaries they occur in, and produces three scores based on the weighted content of new summaries. We present an automated method that is more efficient, more transparent, and more complete than previous automated pyramid methods. It is tested on a new dataset of student summaries, and historical NIST data from extractive summarizers.
%R 10.18653/v1/K19-1038
%U https://aclanthology.org/K19-1038
%U https://doi.org/10.18653/v1/K19-1038
%P 404-418

Markdown (Informal)

[Automated Pyramid Summarization Evaluation](https://aclanthology.org/K19-1038) (Gao et al., CoNLL 2019)

• Automated Pyramid Summarization Evaluation (Gao et al., CoNLL 2019)

ACL

• Yanjun Gao, Chen Sun, and Rebecca J. Passonneau. 2019. Automated Pyramid Summarization Evaluation. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 404–418, Hong Kong, China. Association for Computational Linguistics.