- Abstract:
-
We describe a set of experiments using machine learning techniques for the task of extractive summarisation. The research is part of a summarisation project for which we use a corpus of judgments of the UK House of Lords. We present classification results for na ıve Bayes and maximum entropy and we explore methods for scoring the summary-worthiness of a sentence. We present sample output from the system, illustrating the utility of rhetorical status information, which provides a means for structuring summaries and tailoring them to different types of users
- Links To Paper
- 1st Link
- Bibtex format
- @InProceedings{EDI-INF-RR-0620,
- author = {
Benjamin Hachey
and Claire Grover
},
- title = {Automatic Legal Text Summarisation: Experiments with Summary Structuring},
- book title = {Proceedings of ICAIL 2005 (International Conference on Artificial Intelligence and Law)},
- publisher = {ACM},
- year = 2005,
- month = {Jun},
- pages = {75-84},
- url = {http://www.ltg.ed.ac.uk/SUM/PUBS/icail05-author.pdf},
- }
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