Sentence Extraction for Legal Text Summarisation
Abstract
We describe a system for generating extractive summaries of texts in the legal domain, focusing on the relevance classifier, which determines which sentences are abstract-worthy. We experiment with naive Bayes and maximum entropy estimation toolkits and explore methods for selecting abstract-worthy sentences in rank order. Evaluation using standard accuracy measures and using correlation confirm the utility of our approach, but suggest different optimal configurations.
Cite
Text
Hachey and Grover. "Sentence Extraction for Legal Text Summarisation." International Joint Conference on Artificial Intelligence, 2005.Markdown
[Hachey and Grover. "Sentence Extraction for Legal Text Summarisation." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/hachey2005ijcai-sentence/)BibTeX
@inproceedings{hachey2005ijcai-sentence,
title = {{Sentence Extraction for Legal Text Summarisation}},
author = {Hachey, Ben and Grover, Claire},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2005},
pages = {1686-1687},
url = {https://mlanthology.org/ijcai/2005/hachey2005ijcai-sentence/}
}