@inproceedings{polsley-etal-2016-casesummarizer,
    title = "{C}ase{S}ummarizer: A System for Automated Summarization of Legal Texts",
    author = "Polsley, Seth  and
      Jhunjhunwala, Pooja  and
      Huang, Ruihong",
    editor = "Watanabe, Hideo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/C16-2054/",
    pages = "258--262",
    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."
}