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Indian Legal Documents Corpus for Court Judgment Prediction and Summarization: CJPS
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Author(s): Ayushi Gautam (Vellore Institute of Technology, Chennai, India), Vishali Sharma (Vellore Institute of Technology, Chennai, India), Piyali Saha (Vellore Institute of Technology, Chennai, India), Atul R. Patel (Vellore Institute of Technology, Chennai, India)and Sujithra Kanmani (Vellore Institute of Technology, Chennai, India)
Copyright: 2025
Pages: 20
Source title:
Enhancing Automated Decision-Making Through AI
Source Author(s)/Editor(s): Shalin Hai-Jew (Sedgwick County, USA)
DOI: 10.4018/979-8-3693-6230-3.ch011
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Abstract
We propose the development of an automated system aimed at assisting judges in predicting case outcomes to streamline the judicial process. To ensure practical utility, this system should provide explanations for its predictions. To support research in this area, we introduce the Indian Legal Documents Corpus (ILDC), a substantial collection comprising 35,000 Indian Supreme Court cases, annotated with their original court decisions. Additionally, a subset of this corpus serves as a test set, annotated with expert-generated explanations for reference. Building upon the ILDC, we present the Court Judgment Prediction (CJP) task, which involves the automated prediction of case outcomes. We conduct experiments using various baseline models for case prediction and introduce a hierarchical occlusion analysis of explanations generated by our proposed algorithm. This analysis reveals a notable disparity between the algorithm's perspective and that of legal experts when explaining judgments, suggesting promising avenues for future research in this domain.
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