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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.24908 |
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| _version_ | 1866918150684016640 |
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| author | García, Andrés Fernández de la Rosa, Javier Gonzalo, Julio Morante, Roser Amigó, Enrique Benito-Santos, Alejandro Carrillo-de-Albornoz, Jorge Fresno, Víctor Ghajari, Adrian Marco, Guillermo Plaza, Laura Salido, Eva Sánchez |
| author_facet | García, Andrés Fernández de la Rosa, Javier Gonzalo, Julio Morante, Roser Amigó, Enrique Benito-Santos, Alejandro Carrillo-de-Albornoz, Jorge Fresno, Víctor Ghajari, Adrian Marco, Guillermo Plaza, Laura Salido, Eva Sánchez |
| contents | 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 ``Bolet\'ın 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\%). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24908 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BOE-XSUM: Extreme Summarization in Clear Language of Spanish Legal Decrees and Notifications García, Andrés Fernández de la Rosa, Javier Gonzalo, Julio Morante, Roser Amigó, Enrique Benito-Santos, Alejandro Carrillo-de-Albornoz, Jorge Fresno, Víctor Ghajari, Adrian Marco, Guillermo Plaza, Laura Salido, Eva Sánchez Computation and Language 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 ``Bolet\'ın 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\%). |
| title | BOE-XSUM: Extreme Summarization in Clear Language of Spanish Legal Decrees and Notifications |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.24908 |