Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland

ACL ARR 2025 May Submission6914 Authors

20 May 2025 (modified: 03 Jul 2025)ACL ARR 2025 May SubmissionEveryoneRevisionsBibTeXCC BY 4.0
Abstract: Legal research depends on headnotes—concise summaries that help lawyers quickly identify relevant cases. Yet, many court decisions lack them due to the high cost of manual annotation. To address this gap, we introduce the Swiss Landmark Decisions Summarization (SLDS) dataset: 20K rulings from the Swiss Federal Supreme Court, each with headnotes in German, French, and Italian. SLDS has the potential to significantly improve access to legal information and transform legal research in Switzerland. We fine-tune open models (Qwen2.5, Llama 3.2, Phi-3.5) and compare them to larger general-purpose and reasoning-tuned LLMs, including GPT-4o, Claude 3.5 Sonnet, and the open-source DeepSeek R1. Using an LLM-as-a-Judge framework, we find that fine-tuned models perform well in terms of lexical similarity, while larger models generate more legally accurate and coherent summaries. Interestingly, reasoning-focused models show no consistent benefit, suggesting that factual precision is more important than deep reasoning in this task. We release SLDS under a CC BY 4.0 license to support future research in cross-lingual legal summarization.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: Generation, Multilingualism and Cross-Lingual NLP, Dataset, Resources and Evaluation, Summarization
Contribution Types: Data resources
Languages Studied: German, French, Italian
Submission Number: 6914
Loading