Official court press releases from Germany's highest courts present and explain judicial rulings to the public, as well as to expert audiences. Prior NLP efforts emphasize technical headnotes, ignoring citizen-oriented communication needs. We introduce CourtPressGER, a 6.4k dataset of triples: rulings, human-drafted press releases, and synthetic prompts for LLMs to generate comparable releases. This benchmark trains and evaluates LLMs in generating accurate, readable summaries from long judicial texts. We benchmark small and large LLMs using reference-based metrics, factual-consistency checks, LLM-as-judge, and expert ranking. Large LLMs produce high-quality drafts with minimal hierarchical performance loss; smaller models require hierarchical setups for long judgments. Initial benchmarks show varying model performance, with human-drafted releases ranking highest.
@article{arxiv.2512.09434,
title = {CourtPressGER: A German Court Decision to Press Release Summarization Dataset},
author = {Sebastian Nagl and Mohamed Elganayni and Melanie Pospisil and Matthias Grabmair},
journal= {arXiv preprint arXiv:2512.09434},
year = {2025}
}
Comments
Preprint - This contribution was accepted at JURIX AI4A2J Workshop 2025