Can LLMs Create Legally Relevant Summaries and Analyses of Videos?

Lyra Hoeben-Kuil, Gijs van Dijck, Jaromir Savelka, Johanna Gunawan, Konrad Kollnig +4 more
11/15/2025
cs.MMcs.AIcs.CV

No official code found. Best implementation: maastrichtlawtech/jurix2025_LLM_video_analysis (0 stars, last commit 2025-11-14).

Abstract

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.

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Code Implementations(1)

This repository contains the code to replicate the experiments in the paper "Can LLMs Create Legally Relevant Summaries and Analyses of Videos?", forthcoming at JURIX 2025.

00Jupyter NotebookOct 23, 202511 months ago

Cite this paper

@article{hoebenkuil2025llms,
  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},
  year   = {2025},
  eprint = {2511.13772},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.13772v1}
}

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