Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in S witzerland (2025)

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.

## introduction a significant part of legal work involves research, where lawyers must find similar cases and navigate numerous judicial decisions, especially when interpreting laws with room for debate. due to the time-intensive nature of this task, they usually rely on judgment summaries. however, creating these summaries is labor intensive and requires the expertise of judges and clerks, who are already burdened with a large caseload @xcite and time pressure @xcite . to alleviate this increasing need for efficient ways to navigate large amounts of legal texts, legal document summarization has become a critical area of interest in nlp @xcite . over 5 family and two frontier models evaluated on the slds test set. while fine-tuning dominates outcomes in terms of lexical metrics, the smaller fine-tuned models do not yet reach the same output quality as their larger pre-trained counterparts, as indicated by the llm-as-a-judge @xcite score. the years, researchers have made significant strides in both extractive and abstractive summarization of legal texts. earlier work focused on extracting key sentences to create concise summaries @xcite @xcite , while recent advancements have turned toward abstractive methods, which generate condensed paraphrases of the most important information in a document @xcite @xcite @xcite . datasets with legal documents and their corresponding summaries have been instrumental in enabling these advancements, yet they primarily focus on monolingual corpora or multiple jurisdictions. therefore, existing datasets do not adequately address the unique challenges posed by multilingual jurisdictions, such as switzerland, where legal decisions are written in multiple languages and need to be summarized consistently. this gap is particularly relevant because many legal nlp tools and models are trained on englishcentric datasets, which may not reliably generalize to cross-lingual environments. we introduce swiss landmark decision summarization (slds), a large-scale multilingual dataset of swiss supreme court cases in german, french, and italian, featuring headnotes that summarize key legal points and laws. by focusing on these concise legal digests, slds facilitates cross-lingual legal summarization research and supports the development of tools for professionals working across language barriers. the dataset is publicly available under a cc by 4.0 license. 1 contributions our contributions are two-fold: ## related work research on legal text summarization has increasingly shifted toward abstractive methods, supported by the emergence of dedicated datasets. among monolingual english corpora, billsum @xcite offers 22k u.s. congressional and state bills with summaries, enabling crossdomain transfer for legal summarization. multi-lexsum @xcite focuses on long civil rights lawsuits and supports multi-length evaluations. @xcite extracted key passages from 430k u.s. court opinions, favoring reinforcement learning methods, although their dataset is not publicly available. rulingbr (de vargas feijó and moreira, 2018) includes 10k brazilian supreme court rulings with structured summaries. law-suit @xcite ) contains 14k italian 1 https://huggingface.co/datasets/ipst/slds verdicts with expert-authored maxims from the constitutional court. multilingual datasets include eur-lex-sum @xcite , which covers 24 eu languages and aligns 375 legal acts. unlike court rulings, these acts follow a more structured format. in contrast, our dataset emphasizes case law within a single jurisdiction, offering over 13 times more french-to-german and more than twice as many italian-to-german examples than eur-lex-sum. mildsum @xcite addresses language barriers in india by translating 3k english judgments to hindi. a key result was that summarizethen-translate outperformed direct cross-lingual summarization. unlike mildsum, our dataset excludes english and uses headnotes, which are harder to generate than summaries due to their legal specificity and structural requirements, making the task more challenging given the dominance of english in pretraining corpora. ## data we introduce slds, a novel dataset for crosslingual summarization in the legal domain. it comprises over 20k landmark decisions published by the sfsc in german, french, or italian, each accompanied by paragraph-aligned summaries written by clerks and judges in all three languages. this dataset provides a valuable resource for studying cross-lingual summarization, a relatively underexplored area in legal nlp. unlike datasets such as eur-lex-sum, which focus on legislation, slds centers on judicial decisions, making it particularly relevant for developing tools to assist legal practitioners and researchers working with court rulings. ## experimental setup we evaluate four frontier large language models (llms) (gpt-4o, claude 3.5 sonnet, deepseek r1, o3-mini) in a one-shot setting and fine-tune three small language models (slms) (llama 3.2 3b, qwen2.5 3b, and phi-3.5-mini) on the slds training split. to assess the effect of model size, we fine-tune additional qwen2.5 variants (0.5b -14b) and evaluate them in a zero-shot setting. 5 appendix f details the model versions, decoding parameters, and one-shot prompting. finetuning hyperparameters are listed in appendix g. ## human expert evaluation we perform two human expert evaluations. the first is based on the same evaluation process the llm judge also follows. two lawyers assess three generated headnotes across 63 samples. this evaluation only considers the generated and the original headnote without taking into account the actual text of the landmark decision, assuming the gold headnote is the ideal headnote and that any deviation should be penalized. we refer to this evaluation as human-as-a-judge. in the second evaluation which we will refer to as contextualized human analysis, another lawyer looked at six of those 63 samples and performed an in-depth analysis which involved studying the decision text as well. ## conclusions and future work we introduce slds, a large-scale cross-lingual resource for judicial summarization. we benchmark fine-tuned and proprietary models, revealing a trade-off between lexical similarity and legal accuracy. while fine-tuned models perform well on traditional summarization metrics, they struggle with legal correctness, as shown by our llm-as-a-judge evaluation. proprietary models demonstrated higher legal faithfulness and structured output. notably, reasoning models did not significantly outperform general-purpose llms, suggesting that headnote generation requires domain-specific precision rather than complex reasoning.