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        <title>Neural Network-Based Generation of Sport Summaries: A Preliminary Study</title>
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    <name type="personal">
        <namePart type="given">David</namePart>
        <namePart type="given">Stéphane</namePart>
        <namePart type="family">Belemkoabga</namePart>
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        <namePart type="given">Aurélien</namePart>
        <namePart type="family">Bossard</namePart>
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    <name type="personal">
        <namePart type="given">Abdallah</namePart>
        <namePart type="family">Essa</namePart>
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    <name type="personal">
        <namePart type="given">Christophe</namePart>
        <namePart type="family">Rodrigues</namePart>
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    <name type="personal">
        <namePart type="given">Kévin</namePart>
        <namePart type="family">Sylla</namePart>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>This paper presents a global summarization method for live sport commentaries for which we have a human-written summary available. This method is based on a neural generative summarizer. The amount of data available for training is limited compared to corpora commonly used by neural summarizers. We propose to help the summarizer to learn from a limited amount of data by limiting the entropy of the input texts. This step is performed by a classification into categories derived by a detailed analysis of the human-written summaries. We show that the filtering helps the summarization system to overcome the lack of resources. However, several improving points have emerged from this preliminary study, that we discuss and plan to implement in future work.</abstract>
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        <date>2021-09</date>
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%0 Conference Proceedings
%T Neural Network-Based Generation of Sport Summaries: A Preliminary Study
%A Belemkoabga, David Stéphane
%A Bossard, Aurélien
%A Essa, Abdallah
%A Rodrigues, Christophe
%A Sylla, Kévin
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F belemkoabga-etal-2021-neural
%X This paper presents a global summarization method for live sport commentaries for which we have a human-written summary available. This method is based on a neural generative summarizer. The amount of data available for training is limited compared to corpora commonly used by neural summarizers. We propose to help the summarizer to learn from a limited amount of data by limiting the entropy of the input texts. This step is performed by a classification into categories derived by a detailed analysis of the human-written summaries. We show that the filtering helps the summarization system to overcome the lack of resources. However, several improving points have emerged from this preliminary study, that we discuss and plan to implement in future work.
%U https://aclanthology.org/2021.ranlp-1.18
%P 147-154

Markdown (Informal)

[Neural Network-Based Generation of Sport Summaries: A Preliminary Study](https://aclanthology.org/2021.ranlp-1.18) (Belemkoabga et al., RANLP 2021)

• Neural Network-Based Generation of Sport Summaries: A Preliminary Study (Belemkoabga et al., RANLP 2021)

ACL

• David Stéphane Belemkoabga, Aurélien Bossard, Abdallah Essa, Christophe Rodrigues, and Kévin Sylla. 2021. Neural Network-Based Generation of Sport Summaries: A Preliminary Study. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 147–154, Held Online. INCOMA Ltd..