Classification of Legal Judgement Summary using Conditional Random Field Algorithm
DOI:
https://doi.org/10.26438/ijcse/v6i5.2333Keywords:
Classification, CRF, LDA, Fuzzy Logic, Legal JudgementAbstract
An Automatic Summary generation process creates a shortened version of the text using a Digital programming Technology, with the aim of holding the most advanced important points of the original text. In a Common Law system, previous judgments were referred to the current case arguments as well as decision making. Thus there is a need to view the previous judgments and to grasp and analyze the important points present in the legal judgments. Text Summarization technique helps the legal experts to read the key points present in a judgment just by reading the Head note generated by the system. Such techniques save the time as well as the manpower. In this paper, an automatic Legal Judgment Summarization system was implemented and tested by Fuzzy Logic, Classification and Segmentation techniques among that based on the experimental study Fuzzy Logic and Conditional Random Field Algorithm produces a meaningful summary.
References
Mohammed Zaki J, Wagner Meira, “Data Mining and Analysis: Fundamental Concepts and Algorithms”, Cambridge University Press, 2014.
Mani I, Maybury M, “Advances in Automatic Text Summarization”, Cambridge MIT Press, 1999.
Shams R, Elsayed A, and Akter Q.M, “A Corpus-based evaluation of a domain-specific text to knowledge mapping prototype”, Special Issue of Journal of Computers, Academy Publisher, 2010.
Patil M.S, “A Hybrid Approach for Extractive Document Summarization Using Machine Learning and Clustering Technique”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2), PP: 1584-1586, 2014.
Viterbi A.J, “Error Bounds for Convolution Codes and Asymptotically Optimal Decoding Algorithm”, IEEE Transactions on Information Processing, vol. 13, pp. 260-269, 1967.
Ryan M.S and Nudd G.R, “The Viterbi Algorithm” Warwick Research report 238, Department of Computer Science, University of Warwick, Coventry, England, Feb.1993.
Nenkova, Lucy Vanderwende, and Kathleen McKeown. "A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization". In SIGIR 2006, New York, NY, USA, ACM, PP: 573-580, 2006.
Kyoomarsi F, Khosravi H, Eslami E and Davoudi M, “Extraction-Based Text Summarization using Fuzzy Analysis”, Iranian Journal of Fuzzy Systems Vol. 7, No. 3, pp. 15-32, 2010.
Ladda Suanmali, Naomie Salim, and Mohammed Salem Binwahlan, "Fuzzy Logic Based Method for Improving Text Summarization", (IJCSIS) International Journal of Computer Science and Information Security, Vol. 2, No. 1, 2009.
Ravi Kumar V and Raghuveer K, “Legal Documents Clustering using Latent Dirichlet Allocation”, International Journal of Applied Information Systems, 2012, P: 34-37
Pramod Pardeshi, Ujwala Patil, "Fuzzy Association Rule Mining- A Survey", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.13-18, 2017.
A. Yadav, V.K. Harit, "Fault Identification in Sub-Station by Using Neuro-Fuzzy Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.6, pp.1-7, 2016
Amit Palve, Rohini D.Sonawane, Amol D. Potgantwar, "Sentiment Analysis of Twitter Streaming Data for Recommendation using, Apache Spark", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.99-103, 2017.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
