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        <title>Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland</title>
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    <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 containing 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.</abstract>
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%0 Conference Proceedings
%T Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland
%A Rolshoven, Luca
%A Rasiah, Vishvaksenan
%A Bose, Srinanda Brügger
%A Hostettler, Sarah
%A Burkhalter, Lara
%A Stürmer, Matthias
%A Niklaus, Joel
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F rolshoven-etal-2025-unlocking
%X 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 containing 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.
%U https://aclanthology.org/2025.findings-emnlp.832/
%P 15382-15411

Markdown (Informal)

[Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland](https://aclanthology.org/2025.findings-emnlp.832/) (Rolshoven et al., Findings 2025)

• Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland (Rolshoven et al., Findings 2025)

ACL

• Luca Rolshoven, Vishvaksenan Rasiah, Srinanda Brügger Bose, Sarah Hostettler, Lara Burkhalter, Matthias Stürmer, and Joel Niklaus. 2025. Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15382–15411, Suzhou, China. Association for Computational Linguistics.