Abstract
This paper presents the challenges and possibilities of a novel summarisation task: automatic generation of catchphrases for legal documents. Catchphrases are meant to present the important legal points of a document with respect of identifying precedents. Automatically generating catchphrases for legal case reports could greatly assist in searching for legal precedents, as many legal texts do not have catchphrases attached. We developed a corpus of legal (human-generated) catchphrases (provided with the submission), which lets us compute statistics useful for automatic catchphrase extraction. We propose a set of methods to generate legal catchphrases and evaluate them on our corpus. The evaluation shows a recall comparable to humans while still showing a competitive level of precision, which is very encouraging. Finally, we introduce a novel evaluation method for catchphrases for legal texts based on the known Rouge measure for evaluating summaries of general texts.
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References
Ashley, K.D., Brüninghaus, S.: Automatically classifying case texts and predicting outcomes. Artif. Intell. Law 17(2), 125–165 (2009)
Brüninghaus, S., Ashley, K.D.: Improving the representation of legal case texts with information extraction methods. In: ICAIL 2001: Proceedings of the 8th International Conference on Artificial Intelligence and Law, pp. 42–51. ACM, New York (2001)
Ceylan, H., Mihalcea, R., Özertem, U., Lloret, E., Palomar, M.: Quantifying the limits and success of extractive summarization systems across domains. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT 2010, pp. 903–911. Association for Computational Linguistics, Stroudsburg (2010)
Elkiss, A., Shen, S., Fader, A., Erkan, G., States, D., Radev, D.: Blind men and elephants: What do citation summaries tell us about a research article? J. Am. Soc. Inf. Sci. Technol. 59(1), 51–62 (2008)
Farzindar, A., Lapalme, G.: Letsum, an automatic legal text summarizing system. In: The Seventeenth Annual Conference on Legal Knowledge and Information Systems, JURIX 2004, p. 11. IOS Pr. Inc. (2004)
Farzindar, A., Lapalme, G.: Machine translation of legal information and its evaluation. In: Advances in Artificial Intelligence, pp. 64–73 (2009)
Galgani, F., Hoffmann, A.: Lexa: Towards Automatic Legal Citation Classification. In: Li, J. (ed.) AI 2010. LNCS, vol. 6464, pp. 445–454. Springer, Heidelberg (2010)
Greenleaf, G., Mowbray, A., King, G., Van Dijk, P.: Public Access to Law via Internet: The Australian Legal Information Institute. Journal of Law and Information Science 6, 49 (1995)
Hachey, B., Grover, C.: Extractive summarisation of legal texts. Artif. Intell. Law 14(4), 305–345 (2006)
Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Moens, M.-F., Szpakowicz, S. (eds.) Text Summarization Branches Out: Proceedings of the ACL 2004 Workshop, pp. 74–81. Association for Computational Linguistics, Barcelona (2004)
Moens, M.-F.: Innovative techniques for legal text retrieval. Artificial Intelligence and Law 9(1), 29–57 (2001)
Moens, M.-F.: Summarizing court decisions. Inf. Process. Manage. 43(6), 1748–1764 (2007)
Mohammad, S., Dorr, B., Egan, M., Hassan, A., Muthukrishan, P., Qazvinian, V., Radev, D., Zajic, D.: Using citations to generate surveys of scientific paradigms. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Boulder, Colorado, pp. 584–592 (June 2009)
Olsson, J.L.T.: Guide To Uniform Production of Judgments, 2nd edn. Australian Institute of Judicial Administration, Carlton South (1999)
Palau, R.M., Moens, M.F.: Argumentation mining: the detection, classification and structure of arguments in text. In: ICAIL 2009: Proceedings of the 12th International Conference on Artificial Intelligence and Law, pp. 98–107. ACM, New York (2009)
Uyttendaele, C., Moens, M., Dumortier, J.: Salomon: automatic abstracting of legal cases for effective access to court decisions. Artificial Intelligence and Law 6(1), 59–79 (1998)
Zhang, P., Koppaka, L.: Semantics-based legal citation network. In: ICAIL 2007: Proceedings of the 11th International Conference on Artificial Intelligence and Law, pp. 123–130. ACM, New York (2007)
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Galgani, F., Compton, P., Hoffmann, A. (2012). Towards Automatic Generation of Catchphrases for Legal Case Reports. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28601-8_35
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DOI: https://doi.org/10.1007/978-3-642-28601-8_35
Publisher Name: Springer, Berlin, Heidelberg
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