Knowledge Representation for Legal Document Summarization
DOI:
https://doi.org/10.55524/ijircst.2023.11.4.11Keywords:
Ripple-Down-Rules, Rhetorical Roles, Legal Document SummarizationAbstract
This paper presents a novel approach for legal document summarization. Proposed approach is based on Ripple-Down Rules (RDR). It is an incremental knowledge acquisition method. RDR allows us to quickly build extendable knowledge base using classification rules. The classification rules are written using a set of features. Summary is generated using the identified rhetorical roles in the document. Experiments demonstrate that the RDR based Legal Document summarization approach outperforms the supervised and unsupervised machine learning models.
Downloads
References
P. Bhattacharya, K. Hiware, S. Rajgaria, N. Pochhi, K. Ghosh, and S. Ghosh, “A comparative study of summarization algorithms applied to legal case judgments,” in ECIR, 2019.
P. Compton and B. Jansen, “Knowledge in context: A strategy for expert system maintenance,” in Australian Joint Conference on Artificial Intelligence, 1988.
M. Saravanan, B. Ravindran, and S. Raman, “Automatic identification of rhetorical roles using conditional random fields for legal document summarization,” in Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I, 2008. [Online]. Available: https://www.aclweb.org/anthology/I08-1063
P. Bhattacharya, S. Paul, K. Ghosh, S. Ghosh, and A. Wyner, “Identification of rhetorical roles of sentences in indian legal judgments,” in Legal Knowledge and Information Systems - JURIX 2019: The Thirtysecond Annual Conference, Madrid, Spain, December 11-13, 2019, vol. 322. IOS Press, 2019, pp. 3–12.
B. Hachey and C. Grover, “A rhetorical status classifier for legal text summarisation,” in Text Summarization Branches Out. Barcelona, Spain: Association for Computational Linguistics, Jul. 2004, pp. 35–42. [Online]. Available: https://www.aclweb.org/anthology/W04-1007
P. Bhattacharya, S. Poddar, K. Rudra, K. Ghosh, and S. Ghosh, “Incorporating domain knowledge for extractive summarization of legal case documents,” CoRR, vol. abs/2106.15876, 2021. [Online]. Available: https://arxiv.org/abs/2106.15876
S. Teufel and M. Moens, “Summarizing scientific articles: Experiments with relevance and rhetorical status,” Computational Linguistics, vol. 28,p. 2002, 2002.
B. Hachey and C. Grover, “Extractive summarisation of legal texts,” Artif. Intell. Law, vol. 14, no. 4, pp. 305–345, 2006.
S. D. Kavila, V. Puli, G. S. V. Prasada Raju, and R. Bandaru, “An automatic legal document summarization and search using hybrid system,” in Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), S. C. Satapathy, S. K. Udgata, and B. N. Biswal, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 229–236.
P. Compton and R. Jansen, “A philosophical basis for knowledge acquisition,” Knowledge Acquisition, vol. 2, no. 3, p. 241–257, 1990.
D. Richards, “Two decades of ripple down rules research,” The Knowledge Engineering Review, vol. 24, pp. 159–184, Jun. 2009.
B. H. Kang, W. Gambetta, and P. Compton, “Verification and validation with ripple-down rules,” Int. J. Hum. Comput. Stud., vol. 44, no. 2, pp. 257–269, 1996.
F. Galgani, P. Compton, and A. G. Hoffmann, “LEXA: building knowledge bases for automatic legal citation classification,” Expert Syst. Appl., vol. 42, no. 17-18, pp. 6391–6407, 2015.
F. Galgani, P. Compton, and A. Hoffmann, “Knowledge acquisition for categorization of legal case reports,” in Knowledge Management and Acquisition for Intelligent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 118–132.
F. Galgani, P. Compton, and A. G. Hoffmann, “Combining different summarization techniques for legal text,” in Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data. Avignon, France: Association for Computational Linguistics, Apr. 2012, pp. 115–123. [Online]. Available: https://aclanthology.org/W12-0515
M. Grootendorst, “Keybert: Minimal keyword extraction with bert.” 2020. [Online]. Available: https://doi.org/10.5281/zenodo.4461265
S. Rose, D. Engel, N. Cramer, and W. Cowley, “Automatic keyword extraction from individual documents,” in Text Mining. Applications and Theory, M. W. Berry and J. Kogan, Eds. John Wiley and Sons, Ltd, 2010, pp. 1–20. [Online]. Available: http://dx.doi.org/10.1002/9780470689646.ch1
M. J. B. II, D. M. Katz, and E. M. Detterman, “Lexnlp: Natural language processing and information extraction for legal and regulatory texts,” CoRR, vol. abs/1806.03688, 2018.
F. Galgani, P. Compton, and A. G. Hoffmann, “Combining different summarization techniques for legal text,” in Proceedings of HYBRID12, 2012, p. 115–123.
C.-Y. Lin, “ROUGE: A package for automatic evaluation of summaries,” in Text Summarization Branches Out. Barcelona, Spain: Association for Computational Linguistics, Jul. 2004, pp. 74–81. [Online]. Available: https://aclanthology.org/W04-1013
G. Erkan and D. Radev, “Lexrank: Graph-based lexical centrality as salience in text summarization,” Journal of Artificial Intelligence Research - JAIR, vol. 22, 09 2011.
S. A. Takale, S. A. Thorat, and R. S. Sajjan, “Legal document summarization using ripple down rules,” in 2022 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), 2022, pp. 78–83.
Downloads
Published
Issue
Section
How to Cite
