BOE-XSUM: Extreme Summarization in Clear Language of Spanish Legal Decrees and Notifications
This addresses the problem of information overload for users of Spanish legal documents, but it is incremental as it applies existing summarization methods to a new dataset.
The authors tackled the lack of concise summaries for Spanish legal documents by creating BOE-XSUM, a dataset of 3,648 plain-language summaries from Spain's official gazette, and found that fine-tuned LLMs, such as BERTIN GPT-J 6B, achieved a 24% performance gain over zero-shot models, with accuracies of 41.6% vs. 33.5%.
The ability to summarize long documents succinctly is increasingly important in daily life due to information overload, yet there is a notable lack of such summaries for Spanish documents in general, and in the legal domain in particular. In this work, we present BOE-XSUM, a curated dataset comprising 3,648 concise, plain-language summaries of documents sourced from Spain's ``Boletín Oficial del Estado'' (BOE), the State Official Gazette. Each entry in the dataset includes a short summary, the original text, and its document type label. We evaluate the performance of medium-sized large language models (LLMs) fine-tuned on BOE-XSUM, comparing them to general-purpose generative models in a zero-shot setting. Results show that fine-tuned models significantly outperform their non-specialized counterparts. Notably, the best-performing model -- BERTIN GPT-J 6B (32-bit precision) -- achieves a 24\% performance gain over the top zero-shot model, DeepSeek-R1 (accuracies of 41.6\% vs.\ 33.5\%).