Improving Legal Document Summarization Using Graphical Models

Frontiers in Artificial Intelligence and Applications

Volumn 152, Issue , 2006, Pages 51-60

  Saravanan, M ^a   Ravindran, B ^a   Raman, S ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

Author keywords

Automatic Text Summarization; CRF model; Legal domain; Rhetorical roles

Indexed keywords

DATA MINING; DOMAIN KNOWLEDGE; NATURAL LANGUAGE PROCESSING SYSTEMS; RANDOM PROCESSES; TEXT PROCESSING;

AUTOMATIC TEXT SUMMARIZATION; CONDITIONAL RANDOM FIELD MODEL; DOCUMENT SUMMARIZATION; GRAPHICAL MODEL; LEGAL DOCUMENTS; LEGAL DOMAINS; PROBABILISTIC GRAPHICAL MODELS; RANDOM FIELD MODEL; RHETORICAL ROLE; TEXT-MINING;

GRAPHIC METHODS;

EID: 85139046202     PISSN: 09226389     EISSN: 18798314     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (14)

1. M. Brunn, Y. Chali and C.J. Pinchak, Text Summarization using lexical chains, Workshop on Text summarization in conjuction with the ACM SIGIR Conference, New Orleans, Louisiana, 2001.
2. Claire Grover, Ben Hachey, la n Hughson and Chris Korycinski, Automatic summarization of legal documents, In Proceedings of International Conference on Artificial Intelligence and Law, Edinburgh, UK, 2003.
3. Ben Hachey and Claire Grover, Sequence Modeling for sentence classification in a legal summarization system, In Proceedings of the 2005 ACM Symposium on Applied Computing, 2005
4. Yunhua Hu, Hang Li, Yunbo Cao, Li Teng, Dmitrly Meyerzon and Qinghua Zheng, Automatic extraction of titles from general documents using machine learning, Information Processing Management, 42, PP. 1276-1293, 2006.
5. Marie-Francine Moens, Caroline uyttendaele, and Jos Durmortier, Intelligent Information Extraction from legal texts, Information & Communications Technology Law, 9(1), PP. 17-26, 2000.
6. John Lafferty, Andrew McCallum, and Fernando Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, In Proceedings of International Conference on Machine Learning 2001.
7. M. Saravanan, S. Raman, and B.Ravindran, A Probabilistic approach to multi-document summarization for a tiled summary, In Proceedings of International Conference on Computational Intelligence and Multimedia Applications, Los Vegas, USA, Aug 16-18, 2005.
8. C.D. Manning and H. Schutze, Foundations of Statistical Natural Language Processing, The MIT Press, London, England, 2001.
9. V.K. Bhatia, Analyzing Genre: Language Use in Professional Settings, London: Longman, 1993.
10. Simone Teufel and Marc Moens, Summarizing scientifi articles - experiments with relevance and rhetorical status, Association of Computational Linguistics, 28(4), PP. 409-445, 2002.
11. Atefeh Farzindar and Guy Lapalme, Letsum, an automatic legal text summarizing system, Legal Knowledge and Information System, Jurix 2004: The seventeenth Annual Conference, Amsterdam: IOS Press, PP. 11-18, 2004.
12. Fuchun Peng, and Andrew McCallum, Accurate information extraction from research papers using conditional random fields, Information Processing Management, 42(4), PP. 963-979, 2006
13. S.M. Katz, Distribution of content words and phrases in text and language modeling, Natural language Engineering, 2(1), PP. 15-59, 1995.
14. Siegal, Sidney and N.John Jr. Castellan, Nonparametric Statistics for the Behavioral Sciences, McGraw Hill, Berkeley, CA, 2nd Edition., 1988.