Skip to main content
Log in

Salomon: Automatic Abstracting of Legal Cases for Effective Access to Court Decisions

  • Published:
Artificial Intelligence and Law Aims and scope Submit manuscript

Abstract

The SALOMON project is a contribution to the automatic processing of legal texts. Its aim is to automatically summarise Belgian criminal cases in order to improve access to the large number of existing and future cases. Therefore, techniques are developed for identifying and extracting relevant information from the cases. A broader application of these techniques could considerably simplify the work of the legal profession.

A double methodology was used when developing SALOMON: the cases are processed by employing additional knowledge to interpret structural patterns and features on the one hand and by way of occurrence statistics of index terms on the other. As a result, SALOMON performs an initial categorisation and structuring of the cases and subsequently extracts the most relevant text units of the alleged offences and of the opinion of the court. The SALOMON techniques do not themselves solve any legal questions, but they do guide the user effectively towards relevant texts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Baxendale, P. B. 1958. Machine-made index for technical literature - an experiment. IBM Journal of Research and Development2(4): 354-361.

    Google Scholar 

  • Bing, J. 1995. Legal text retrieval and information services. In J. Bing and O. Torvund (eds.), 25 Years Anniversary Anthology in Computers and Law, pp. 525-585. Oslo: TANO.

    Google Scholar 

  • Chinchor, N. 1992. MUC-4 evaluation metrics. In Fourth Message Understanding Conference (MUC-4): Proceedings of a Conference Held in McLean, Virginia June 16-18, 1992, pp. 22-29. San Mateo, CA: Morgan Kaufmann Publishers.

    Google Scholar 

  • Dabney, D. P. 1986. The curse of Thamus: An analysis of full-text legal document retrieval. Law Library Journal78: 5-40.

    Google Scholar 

  • Earl, L. L. 1970. Experiments in automatic extracting and indexing. Information Storage and Retrieval6: 313-334.

    Article  Google Scholar 

  • Edmundson, H. P. 1969. New methods in automatic extracting. Journal of the Association for Computing Machinery16(2): 264-285.

    Google Scholar 

  • Gelbart, D. & Smith, J. C. 1991. Beyond Boolean search: FLEXICON, A legal text-based intelligent system. Third International Conference on Artificial Intelligence & Law. Proceedings of the Conference, pp. 225-234. New York: ACM.

    Google Scholar 

  • Gelbart, D. & Smith, J. C. 1994. Automating the process of abstracting legal cases. International Journal of Law and Information Technology1(3): 332-334.

    Google Scholar 

  • Hearst, M. A. & Plaunt, C. 1993. Subtopic structuring for full-length document access. In R. Korfhage, E. Rasmussen and P. Willett (eds.), Proceedings of the Sixteenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, pp. 59-68. New York: ACM.

    Google Scholar 

  • Jacobs, P. S. 1992. Text-based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Jacobs, P. S. 1993. Using statistical methods to improve knowledge-based news categorization. IEEE Expert8(2): 13-23.

    Article  Google Scholar 

  • Jardine, N. & van Rijsbergen, C. J. 1971. The use of hierarchic clustering in information retrieval. Information Storage and Retrieval7: 217-240.

    Article  Google Scholar 

  • Jones, W. P. & Furnas, G. W. 1987. Pictures of relevance: A geometric analysis of similarity measures. Journal of the American Society for Information Science38(6): 420-442.

    Google Scholar 

  • Kaufman, L. & Rousseeuw, P. J. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. New York: John Wiley & Sons.

    Google Scholar 

  • Kintsch, W. & van Dijk, T. A. 1978. Toward a model of text comprehension and production. Psychological Review85(5): 363-394.

    Article  Google Scholar 

  • Kupiec, J., Pedersen, J. & Chen, F. 1995. A trainable document summarizer. In E. A. Fox, P. Ingwersen and R. Fidel (eds.), Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 68–73. New York: ACM.

    Google Scholar 

  • Lewis, D. D. 1995. Evaluating and Optimizing Autonomous Text Classification Systems. In E. A. Fox, P. Ingwersen and R. Fidel (eds.), Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 246-254. New York: ACM.

    Google Scholar 

  • Luhn, H. P. 1957. A statistical approach to the mechanized encoding and searching of literary information. IBM Journal of Research and Development1(4): 309-317.

    Google Scholar 

  • Luhn, H. P. 1958. The automatic creation of literature abstracts. IBM Journal of Research and Development2: 159-165.

    Google Scholar 

  • Moles, R. N. & Dayal, S. 1992. There is more to life than logic. Journal of Law and Information Science3(2): 188-218.

    Google Scholar 

  • Moens, M.-F. & Uyttendaele, C. (1997). Automatic structuring and categorization as a first step in summarizing legal cases. Information Processing & Management33(6): 727-737.

    Google Scholar 

  • Moens, M.-F., Uyttendaele, C. & Dumortier, J. (1997a). Abstracting of legal cases: The SALOMON experience. In Proceedings of the Sixth International Conference on Artificial Intelligence & Law, pp. 114–122. New York: ACM.

    Google Scholar 

  • Moens, M.-F., Uyttendaele, C. & Dumortier, J. (1997b). Abstracting of legal cases: The potential of clustering based on the selection of representative objects. Technical Report, ICRI, K.U. Leuven.

  • Paice, C. D. 1991. The rhetorical structure of expository text. Informatics 11. The Structuring of Information, ed. K. P. Jones, pp. 1-25. London: Aslib.

    Google Scholar 

  • Pinto Molina, M. 1995. Documentary abstracting: toward a methodological model. Journal of the American Society for Information Science46(3): 225-234.

    Google Scholar 

  • Prikhod’ko, S. M. & Skorokhod’ko, E. F. 1982. Automatic abstracting from analysis of links between phrases. Nauchno-Tekhnicheskaya Informatsiya, Seriya 216(1): 27-32.

    Google Scholar 

  • Salton, G., Allan, J., Buckley, C. & Singhal, A. 1994. Automatic analysis, theme generation, and summarization of machine-readable texts. Science264: 1421-1426.

    Google Scholar 

  • Salton, G., Allan, J. & Singhal, A. 1996. Automatic text decomposition and structuring. Information Processing & Management32(2): 127-138.

    Google Scholar 

  • Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management24(5): 513-523.

    Google Scholar 

  • Sparck Jones, K. 1993. What might be in a summary. In G. Knorz, J. Krause and C. Womser-Hacker (eds.), Information Retrieval 93: Von der Modulierung zum Anwendung, pp. 9-26. Konstanz: Universitätsverlag.

    Google Scholar 

  • Susskind, R. E. 1986. Expert systems in law: A jurisprudential approach to artificial intelligence and legal reasoning. The Modern Law Review49: 168-194.

    Google Scholar 

  • Uyttendaele, C., Gebruers, R. & Moens, M.-F. 1995. SALOMON: An exercise in legal information extraction. Legal, linguistic and information technology aspects. Notes of workshop Information Technology and Law. K. U. Leuven.

  • Van Kralingen, R. W. 1995. Frame-based Conceptual Models of Statute Law. "s-Gravenhage: Kluwer Law International.

    Google Scholar 

  • Visser, P. R. S. 1995. Knowledge Specification for Multiple Legal Tasks."s-Gravenhage: Kluwer Law International.

    Google Scholar 

  • Wang, J. T. L. & Ng, P. 1992. Textpros: An intelligent processing document system. International Journal of Software Engineering and Knowledge Engineering2(2): 171-196.

    Google Scholar 

  • Willett, P. 1980. Document clustering using an inverted file approach. Journal of Information Science2: 223-231.

    Google Scholar 

  • Zeleznikow, J. & Hunter, D. 1992. Rationales for the continued development of legal expert systems. Journal of Law and Information Science3(1): 94-110.

    Google Scholar 

  • Zeleznikow, J. & Hunter, D. 1994. Building Intelligent Legal Information Systems. Deventer, Boston: Kluwer Law and Taxation Publishers.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Uyttendaele, C., Moens, MF. & Dumortier, J. Salomon: Automatic Abstracting of Legal Cases for Effective Access to Court Decisions. Artificial Intelligence and Law 6, 59–79 (1998). https://doi.org/10.1023/A:1008256030548

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008256030548

Navigation