From Judgement's Premises Towards Key Points

TL;DR

This paper introduces new methods for extracting and categorizing key argumentative points from legal judgment texts, advancing NLP techniques in legal analysis.

Contribution

It develops and compares three methods, including an adaptation of a state-of-the-art approach, for key point extraction in legal texts.

Findings

New methods effectively extract key points from legal judgments.

Comparison shows varying performance among the methods.

Evaluation demonstrates potential for improved legal argument analysis.

Abstract

Key Point Analysis(KPA) is a relatively new task in NLP that combines summarization and classification by extracting argumentative key points (KPs) for a topic from a collection of texts and categorizing their closeness to the different arguments. In our work, we focus on the legal domain and develop methods that identify and extract KPs from premises derived from texts of judgments. The first method is an adaptation to an existing state-of-the-art method, and the two others are new methods that we developed from scratch. We present our methods and examples of their outputs, as well a comparison between them. The full evaluation of our results is done in the matching task -- match between the generated KPs to arguments (premises).