CaseSummarizer: A System for Automated Summarization of Legal Texts

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.

[1]  Kam-Fai Wong,et al.  Extractive Summarization Using Supervised and Semi-Supervised Learning , 2008, COLING.

[2]  J. C. Smith,et al.  Beyond boolean search: FLEXICON, a legal tex-based intelligent system , 1991, ICAIL '91.

[3]  Ewan Klein,et al.  Natural Language Processing with Python , 2009 .

[4]  Guy Lapalme,et al.  LetSum, an automatic Legal Text Summarizing system , 2004 .

[5]  Achim G. Hoffmann,et al.  LEXA: Building knowledge bases for automatic legal citation classification , 2015, Expert Syst. Appl..

[6]  Rada Mihalcea,et al.  Language Independent Extractive Summarization , 2005, ACL.

[7]  Paul Compton,et al.  Combining Different Summarization Techniques for Legal Text , 2012 .

[8]  Chin-Yew Lin,et al.  ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.

[9]  Achim G. Hoffmann,et al.  LEXA: Towards Automatic Legal Citation Classification , 2010, Australasian Conference on Artificial Intelligence.

[10]  Dragomir R. Radev,et al.  LexRank: Graph-based Lexical Centrality as Salience in Text Summarization , 2004, J. Artif. Intell. Res..

[11]  Regina Barzilay,et al.  Using Lexical Chains for Text Summarization , 1997 .

[12]  Feifan Liu,et al.  Correlation between ROUGE and Human Evaluation of Extractive Meeting Summaries , 2008, ACL.

[13]  Achim G. Hoffmann,et al.  Citation Based Summarisation of Legal Texts , 2012, PRICAI.

[14]  Marie-Francine Moens,et al.  Abstracting of Legal Cases: The Potential of Clustering Based on the Selection of Representative Objects , 1999, J. Am. Soc. Inf. Sci..

[15]  S. Chitrakala,et al.  A survey on abstractive text summarization , 2016, 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT).

[16]  Ani Nenkova,et al.  A Survey of Text Summarization Techniques , 2012, Mining Text Data.