From Judgement's Premises Towards Key Points
This work addresses the problem of analyzing legal arguments for legal professionals, but it is incremental as it adapts existing methods and focuses on a specific domain.
The paper tackled the task of Key Point Analysis (KPA) in the legal domain by developing methods to extract argumentative key points from judgment texts, with evaluation based on matching generated key points to premises.
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).