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        <title>CaseSummarizer: A System for Automated Summarization of Legal Texts</title>
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    <name type="personal">
        <namePart type="given">Seth</namePart>
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        <namePart type="given">Pooja</namePart>
        <namePart type="family">Jhunjhunwala</namePart>
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    <name type="personal">
        <namePart type="given">Ruihong</namePart>
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            <namePart type="given">Hideo</namePart>
            <namePart type="family">Watanabe</namePart>
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    <abstract>Attorneys, judges, and others in the justice system are constantly surrounded by large amounts of legal text, which can be difficult to manage across many cases. We present CaseSummarizer, a tool for automated text summarization of legal documents which uses standard summary methods based on word frequency augmented with additional domain-specific knowledge. Summaries are then provided through an informative interface with abbreviations, significance heat maps, and other flexible controls. It is evaluated using ROUGE and human scoring against several other summarization systems, including summary text and feedback provided by domain experts.</abstract>
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        <url>https://aclanthology.org/C16-2054</url>
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%0 Conference Proceedings
%T CaseSummarizer: A System for Automated Summarization of Legal Texts
%A Polsley, Seth
%A Jhunjhunwala, Pooja
%A Huang, Ruihong
%Y Watanabe, Hideo
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F polsley-etal-2016-casesummarizer
%X Attorneys, judges, and others in the justice system are constantly surrounded by large amounts of legal text, which can be difficult to manage across many cases. We present CaseSummarizer, a tool for automated text summarization of legal documents which uses standard summary methods based on word frequency augmented with additional domain-specific knowledge. Summaries are then provided through an informative interface with abbreviations, significance heat maps, and other flexible controls. It is evaluated using ROUGE and human scoring against several other summarization systems, including summary text and feedback provided by domain experts.
%U https://aclanthology.org/C16-2054
%P 258-262

Markdown (Informal)

[CaseSummarizer: A System for Automated Summarization of Legal Texts](https://aclanthology.org/C16-2054) (Polsley et al., COLING 2016)

• CaseSummarizer: A System for Automated Summarization of Legal Texts (Polsley et al., COLING 2016)

ACL

• Seth Polsley, Pooja Jhunjhunwala, and Ruihong Huang. 2016. CaseSummarizer: A System for Automated Summarization of Legal Texts. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, pages 258–262, Osaka, Japan. The COLING 2016 Organizing Committee.