Understanding the legally relevant factual basis of an event and conveying it through text is a key skill of legal professionals. This skill is important for preparing forms (e.g., insurance claims) or other legal documents (e.g., court claims), but often presents a challenge for laypeople. Current AI approaches aim to bridge this gap, but mostly rely on the user to articulate what has happened in text, which may be challenging for many. Here, we investigate the capability of large language models (LLMs) to understand and summarize events occurring in videos. We ask an LLM to summarize and draft legal letters, based on 120 YouTube videos showing legal issues in various domains. Overall, 71.7\% of the summaries were rated as of high or medium quality, which is a promising result, opening the door to a number of applications in e.g. access to justice.
@article{arxiv.2511.13772,
title = {Can LLMs Create Legally Relevant Summaries and Analyses of Videos?},
author = {Lyra Hoeben-Kuil and Gijs van Dijck and Jaromir Savelka and Johanna Gunawan and Konrad Kollnig and Marta Kolacz and Mindy Duffourc and Shashank Chakravarthy and Hannes Westermann},
journal= {arXiv preprint arXiv:2511.13772},
year = {2025}
}
Comments
Accepted for publication at JURIX 2025 Torino, Italy. This is the preprint version. Code and data available at: https://github.com/maastrichtlawtech/jurix2025_LLM_video_analysis