Automatic summarisation of legal documents

Claire Grover*, Ben Hachey, Ian Hughson, Chris Korycinski

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

41 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Artificial Intelligence and Law
Number of pages9
Publication statusPublished - 2003
Event9th International Conference on Artificial Intelligence and Law, ICAIL '03 - Scotland, United Kingdom
Duration: 24 Jun 200328 Jun 2003


Other9th International Conference on Artificial Intelligence and Law, ICAIL '03
Country/TerritoryUnited Kingdom


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