Abstract
This paper presents a supervised machine learning approach for summarizing legal documents. A commercial system for the analysis and summarization of legal documents provided us with a corpus of almost 4,000 text and extract pairs for our machine learning experiments. That corpus was pre-processed to identify the selected source sentences in extracts from which we generated legal structured data. We finally describe our sentence classification experiments relying on a Naive Bayes classifier using a set of surface, emphasis, and content features.
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Yousfi-Monod, M., Farzindar, A., Lapalme, G. (2010). Supervised Machine Learning for Summarizing Legal Documents. In: Farzindar, A., Kešelj, V. (eds) Advances in Artificial Intelligence. Canadian AI 2010. Lecture Notes in Computer Science(), vol 6085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13059-5_8
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DOI: https://doi.org/10.1007/978-3-642-13059-5_8
Publisher Name: Springer, Berlin, Heidelberg
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