LawSum: A weakly supervised approach for Indian Legal Document Summarization

TL;DR

This paper introduces a new dataset of Indian legal judgments with summaries and attributes, and proposes a weakly supervised method for summarizing unstructured legal documents, enabling improved legal analytics and retrieval.

Contribution

The work creates a large, annotated dataset of Indian legal judgments and develops an auto-labeling technique for training a weakly supervised summarization model.

Findings

High accuracy in sentence extraction using auto-labeled data

Effective summarization of Indian legal judgments demonstrated

Potential applications in legal retrieval and decision prediction

Abstract

Unlike the courts in western countries, public records of Indian judiciary are completely unstructured and noisy. No large scale publicly available annotated datasets of Indian legal documents exist till date. This limits the scope for legal analytics research. In this work, we propose a new dataset consisting of over 10,000 judgements delivered by the supreme court of India and their corresponding hand written summaries. The proposed dataset is pre-processed by normalising common legal abbreviations, handling spelling variations in named entities, handling bad punctuations and accurate sentence tokenization. Each sentence is tagged with their rhetorical roles. We also annotate each judgement with several attributes like date, names of the plaintiffs, defendants and the people representing them, judges who delivered the judgement, acts/statutes that are cited and the most common citations used to refer the judgement. Further, we propose an automatic labelling technique for identifying sentences which have summary worthy information. We demonstrate that this auto labeled data can be used effectively to train a weakly supervised sentence extractor with high accuracy. Some possible applications of this dataset besides legal document summarization can be in retrieval, citation analysis and prediction of decisions by a particular judge.