Supervised machine learning for summarizing legal documents

Authors:
Mehdi Yousfi-Monod;Atefeh Farzindar;Guy Lapalme
Affiliations:
Laboratoire RALI RALI-DIRO Université de Montréal, Université de Montréal, Montréal, Québec, Canada;NLP Technologies Inc., Montréal, Québec, Canada;Laboratoire RALI RALI-DIRO Université de Montréal, Université de Montréal, Montréal, Québec, Canada
Venue:
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Year:
2010

Citing 6
Cited 3

Abstracting of legal cases: the potential of clustering based on the selection of representative objects

Journal of the American Society for Information Science
The automatic construction of large-scale corpora for summarization research

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Summarizing court decisions

Information Processing and Management: an International Journal
Extractive summarisation of legal texts

Artificial Intelligence and Law - AI & law in eGovernment and eDemocracy part I
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter
An automatic system for summarization and information extraction of legal information

Semantic Processing of Legal Texts

Combining different summarization techniques for legal text

HYBRID '12 Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
Citation based summarisation of legal texts

PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Summarization of legal texts with high cohesion and automatic compression rate

JSAI-isAI'12 Proceedings of the 2012 international conference on New Frontiers in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

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.