VerbCL: A Dataset of Verbatim Quotes for Highlight Extraction in Case Law

TL;DR

This paper introduces VerbCL, a large dataset of verbatim quotes from court opinions, to facilitate highlight extraction and citation analysis in legal texts, addressing resource scarcity in legal NLP tasks.

Contribution

The paper presents VerbCL, a novel dataset derived from court citation graphs, and formulates highlight extraction as a single-document summarization task with baseline results.

Findings

VerbCL enables analysis of citation importance in legal texts.

Highlight extraction can be approached as a summarization task.

Baseline models show promising results on VerbCL.

Abstract

Citing legal opinions is a key part of legal argumentation, an expert task that requires retrieval, extraction and summarization of information from court decisions. The identification of legally salient parts in an opinion for the purpose of citation may be seen as a domain-specific formulation of a highlight extraction or passage retrieval task. As similar tasks in other domains such as web search show significant attention and improvement, progress in the legal domain is hindered by the lack of resources for training and evaluation. This paper presents a new dataset that consists of the citation graph of court opinions, which cite previously published court opinions in support of their arguments. In particular, we focus on the verbatim quotes, i.e., where the text of the original opinion is directly reused. With this approach, we explain the relative importance of different text spans of a court opinion by showcasing their usage in citations, and measuring their contribution to the relations between opinions in the citation graph. We release VerbCL, a large-scale dataset derived from CourtListener and introduce the task of highlight extraction as a single-document summarization task based on the citation graph establishing the first baseline results for this task on the VerbCL dataset.