Open Access   Article

Classification of Legal Judgement Summary using Conditional Random Field Algorithm

S. Santhana Megala1

1 Dept. of Computer Technology, SNMV College of Arts & Science, Coimbatore, Tamil Nadu, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-5 , Page no. 23-33, May-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i5.2333

Online published on May 31, 2018

Copyright © S. Santhana Megala . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: S. Santhana Megala, “Classification of Legal Judgement Summary using Conditional Random Field Algorithm”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.23-33, 2018.

MLA Style Citation: S. Santhana Megala "Classification of Legal Judgement Summary using Conditional Random Field Algorithm." International Journal of Computer Sciences and Engineering 6.5 (2018): 23-33.

APA Style Citation: S. Santhana Megala, (2018). Classification of Legal Judgement Summary using Conditional Random Field Algorithm. International Journal of Computer Sciences and Engineering, 6(5), 23-33.

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Abstract

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.

Key-Words / Index Term

Classification, CRF, LDA, Fuzzy Logic, Legal Judgement

References

[1] Mohammed Zaki J, Wagner Meira, “Data Mining and Analysis: Fundamental Concepts and Algorithms”, Cambridge University Press, 2014.
[2] Mani I, Maybury M, “Advances in Automatic Text Summarization”, Cambridge MIT Press, 1999.
[3] 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.
[4] 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.
[5] Viterbi A.J, “Error Bounds for Convolution Codes and Asymptotically Optimal Decoding Algorithm”, IEEE Transactions on Information Processing, vol. 13, pp. 260-269, 1967.
[6] 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.
[7] 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.

[8] 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.
[9] 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.

[10] Ravi Kumar V and Raghuveer K, “Legal Documents Clustering using Latent Dirichlet Allocation”, International Journal of Applied Information Systems, 2012, P: 34-37
[11] 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.
[12] 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
[13] 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.