March 29, 2024, 4:48 a.m. | T. Y. S. S Santosh, Vatsal Venkatkrishna, Saptarshi Ghosh, Matthias Grabmair

cs.CL updates on arXiv.org arxiv.org

arXiv:2403.19317v1 Announce Type: new
Abstract: Legal professionals face the challenge of managing an overwhelming volume of lengthy judgments, making automated legal case summarization crucial. However, prior approaches mainly focused on training and evaluating these models within the same jurisdiction. In this study, we explore the cross-jurisdictional generalizability of legal case summarization models.Specifically, we explore how to effectively summarize legal cases of a target jurisdiction where reference summaries are not available. In particular, we investigate whether supplementing models with unlabeled target …

abstract arxiv automated beyond case challenge cs.cl explore face however legal making prior professionals study summarization training transfer type

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Data Scientist (Database Development)

@ Nasdaq | Bengaluru-Affluence