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ing of Legal Cases: The Potential of Clustering Based on the Selection of Representative Objects Marie-Francine Moens,* Caroline Uyttendaele, and Jos Dumortier Interdisciplinary Centre for Law & IT (ICRI), K.U. Leuven, Tiensestraat 41, B-3000 Leuven, Belgium. E-mail: {marie-france.moens, caroline.uyttendaele, jos.dumortier}@law.kuleuven.ac.be The SALOMON project automatically summarizes Belgian criminal cases in order to improve access to the large number of existing and future court decisions. SALOMON extracts text units from the case text to form a case summary. Such a case summary facilitates the rapid determination of the relevance of the case or may be employed in text search. An important part of the research concerns the development of techniques for automatic recognition of representative text paragraphs (or sentences) in texts of unrestricted domains. These techniques are employed to eliminate redundant material in the case texts, and to identify informative text paragraphs which are relevant to include in the case summary. An evaluation of a test set of 700 criminal cases demonstrates that the algorithms have an application potential for automatic indexing, abstracting, and text linking.

Abstracting of Legal Cases: The Potential of Clustering Based on the Selection of Representative Objects