Foundations of statistical natural language processing
Foundations of statistical natural language processing
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Distribution of content words and phrases in text and language modelling
Natural Language Engineering
Automatic summarisation of legal documents
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Sequence modelling for sentence classification in a legal summarisation system
Proceedings of the 2005 ACM symposium on Applied computing
A Probabilistic Approach to Multi-document Summarization for Generating a Tiled Summary
ICCIMA '05 Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications
Information extraction from research papers using conditional random fields
Information Processing and Management: an International Journal
Automatic extraction of titles from general documents using machine learning
Information Processing and Management: an International Journal
Using Legal Ontology for Query Enhancement in Generating a Document Summary
Proceedings of the 2007 conference on Legal Knowledge and Information Systems: JURIX 2007: The Twentieth Annual Conference
Query-based opinion summarization for legal blog entries
Proceedings of the 12th International Conference on Artificial Intelligence and Law
Improving legal information retrieval using an ontological framework
Artificial Intelligence and Law
Identification of rhetorical roles for segmentation and summarization of a legal judgment
Artificial Intelligence and Law
Text summarisation in progress: a literature review
Artificial Intelligence Review
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
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In this paper, we propose a novel idea for applying probabilistic graphical models for automatic text summarization task related to a legal domain. Identification of rhetorical roles present in the sentences of a legal document is the important text mining process involved in this task. A Conditional Random Field (CRF) is applied to segment a given legal document into seven labeled components and each label represents the appropriate rhetorical roles. Feature sets with varying characteristics are employed in order to provide significant improvements in CRFs performance. Our system is then enriched by the application of a term distribution model with structured domain knowledge to extract key sentences related to rhetorical categories. The final structured summary has been observed to be closest to 80% accuracy level to the ideal summary generated by experts in the area.