Citation-Based Summarization of Landmark Judgments (2023)

Abstract: 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.

## introduction in a common law legal framework, law professionals scrutinize a plethora of legal case documents to comprehend the court's handling in diverse legal scenarios. these documents customarily span from dozens to hundreds of pages, making them arduous to comprehend. ergo, condensed summaries are a valuable aid for legal research. manually crafting case summaries is an intellectually demanding task that requires deep, intense legal knowledge and experience of law experts. automatic summarization of legal judgments is a practical and potent solution and, therefore, a widely researched problem among nlp researchers. several earlier studies have provided insights into the challenges associated with summarization of judgments @xcite . summarizing indian judgments is tough due to their varied structures, unlike us, uk, australia and canada. landmark judgments are important court rulings that establish novel legal principles, handle remarkable legal issues, mould the understanding of law, and leave a long-lasting effect on jurisprudence. these judgments not only draw public attention but also gather a large number of citations in later judgments. the citing judgments spotlight the substantive arguments and precedents that lend weight to the ruling in the cited judgment. each citation in a citing judgment is an informative source for legal points of the prior case, statutes, or laws to support the decision. summary of a landmark judgment highlights the noteworthy attributes of the judgment and provides insight into issues handled within the case, critical legal points, and arguments presented by the lawyers. related work: legal text summarization has drawn considerable attention, specifically in the indian context @xcite abstractive legal text summarization algoritrhms include legal-led (nsi319, 2021a), legal-pegasus (nsi319, 2021b), legalsumm @xcite . recently, @xcite developed two transformer-based pretrained language models, inlegalbert and in-caselawbert, through the re-training of legal-bert @xcite and caselawbert @xcite , respectively. contributions: we design an unsupervised, extractive algorithm, cb-jsumm, to summarize a landmark judgment by leveraging its incoming citations (sec. 2). we also curate two datasets for citationbased summarization of judgments consisting of 99 (50 + 49) indian judgments, citing judgments, and gold standard (reference) summaries and assess the quality of the algorithmic summaries(sec. 3). ## methodology given a target landmark judgment j to be summarized, and @xmath0, . . . , c t } a set of citing judgments, the goal of citation-based legal text summarizer is to present a condensed representation of j using the citation information available in the citing judgments (c i 's). judgment j is tokenized into n sentences (j 1 , . . . , j n ) and salient sentences are selected for summary jsumm. for citation-based summarization, it is prudent to consider the context in which the judgment is referred. however, extracting context from the citing judgment is a nontrivial task and requires a cautious approach. since the referring sentence may be inadequate representative of the context in which the target judgment j is referred, we consider the entire paragraph where the target judgment is cited. this paragraph is referred as the citing-text-span and individual sentences in citing-text-span are referred as citation sentences or simply citances. all citing-text-spans are harvested from all citing judgments of j and tokenized into sentences. the collection of citances from the citing-text-spans is denoted by s. thus, @xmath1, . . . , s m ) contains all contextual information from all citing judgments contained in c. fig. 1 clarifies the notation and terminology used throughout the paper. ## conclusion we propose cb-jsumm, an extractive and unsupervised algorithm to summarize landmark judgments leveraging contextual information from citing judgments. we evaluate proposed algorithm using two curated dataset and observe encouraging results.