Legal Documents Clustering and Summarization using Hierarchical Latent Dirichlet Allocation

Ravi kumar Venkatesh


In a common law system and in a country like India, decisions made by judges are significant sources of application and understanding of law. Online access to the Indian Legal Judgments in the digital form creates an opportunities and challenges to the both legal community and information technology researchers. This necessitates organizing, analyzing, retrieving relevant judgment and presenting it in a useful manner to the legal community for quick understanding and for taking necessary decision pertaining to a present case. In this paper we propose an approach to cluster legal judgments based on the topics obtained from hierarchical Latent Dirichlet Allocation (hLDA) using similarity measure between topics and documents and to find the summarization of each document using the same topics. The developed topic based clustering model is capable of grouping the legal judgments into different clusters and to generate summarization in effective manner compare to our previous [1] approach.



Latent Dirichlet Allocation (LDA); hierarchical Latent Dirichlet Allocation (hLDA); Legal Documents Clustering; Similarity measure; Legal Document Summarization;

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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