AugAbEx : Way Forward for Extractive Case Summarization

Summarization of legal judgments poses a heavy cognitive burden on law practitioners due to the complexity of the language, context-sensitive legal jargon, and the length of the document. Therefore, the automatic summarization of legal documents has attracted serious attention from natural language processing researchers. Since the abstractive summaries of legal documents generated by deep neural methods remain prone to the risk of misrepresenting nuanced legal jargon or overlooking key contextual details, we envisage a rising trend toward the use of extractive case summarizers. Given the high cost of human annotation for gold standard extractive summaries, we engineer a light and transparent pipeline that leverages existing abstractive gold standard summaries to create the corresponding extractive gold standard versions. The approach ensures that the experts` opinions ensconced in the original gold standard abstractive summaries are carried over to the transformed extractive summaries. We aim to augment seven existing case summarization datasets, which include abstractive summaries, by incorporating corresponding extractive summaries and create an enriched data resource for case summarization research community. To ensure the quality of the augmented extractive summaries, we perform an extensive comparative evaluation with the original abstractive gold standard summaries covering structural, lexical, and semantic dimensions. We also compare the domain-level information of the two summaries. We commit to release the augmented datasets in the public domain for use by the research community and believe that the resource will offer opportunities to advance the field of automatic summarization of legal documents.

Citation-Based Summarization of Landmark Judgments

Landmark judgments are of prime importance in the Common Law System because of their exceptional jurisprudence and frequent references in other judgments. In this work, we leverage contextual references available in citing judgments to create an extractive summary of the target judgment. We evaluate the proposed algorithm on two datasets curated from the judgments of Indian Courts and find the results promising.

Human Centered AI for Indian Legal Text Analytics

Legal research is a crucial task in the practice of law. It requires intense human effort and intellectual prudence to research a legal case and prepare arguments. Recent boom in generative AI has not translated to proportionate rise in impactful legal applications, because of low trustworthiness and and the scarcity of specialized datasets for training Large Language Models (LLMs). This position paper explores the potential of LLMs within Legal Text Analytics (LTA), highlighting specific areas where the integration of human expertise can significantly enhance their performance to match that of experts. We introduce a novel dataset and describe a human centered, compound AI system that principally incorporates human inputs for performing LTA tasks with LLMs.