We report on the SUM project which applies automatic summarisation techniques to the legal domain. We describe our methodology whereby sentences from the text are classified according to their rhetorical role in order that particular types of sentence can be extracted to form a summary. We describe some experiments with judgments of the House of Lords: we have performed automatic linguistic annotation of a small sample set and then hand-annotated the sentences in the set in order to explore the relationship between linguistic features and argumentative roles. We use state-of-the-art NLP techniques to perform the linguistic annotation using XML-based tools and a combination of rule-based and statistical methods. We focus here on the predictive capacity of tense and aspect features for a classifier.
|Title of host publication||Proceedings of the 9th International Conference on Artificial Intelligence and Law|
|Number of pages||9|
|Publication status||Published - 2003|
|Event||9th International Conference on Artificial Intelligence and Law, ICAIL '03 - Scotland, United Kingdom|
Duration: 24 Jun 2003 → 28 Jun 2003
|Other||9th International Conference on Artificial Intelligence and Law, ICAIL '03|
|Period||24/06/03 → 28/06/03|