BOE-XSUM: Extreme Summarization in Clear Language of Spanish Legal
  Decrees and Notifications
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\'{\i}n 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\%).
68db79b0d2bf1f4b15ec774a
Andrés Fernández García
68db79b0d2bf1f4b15ec774b
Javier de la Rosa
claimed_verified
2025-09-30T07:12:32.334Z
5ef3829e518622264685b0cd
https://cdn-avatars.huggingface.co/v1/production/uploads/1593016943046-noauth.jpeg
Javier de la Rosa
user
versae
68db79b0d2bf1f4b15ec774c
Julio Gonzalo
68db79b0d2bf1f4b15ec774d
Roser Morante
68db79b0d2bf1f4b15ec774e
Enrique Amigó
68db79b0d2bf1f4b15ec774f
Alejandro Benito-Santos
68db79b0d2bf1f4b15ec7750
Jorge Carrillo-de-Albornoz
68db79b0d2bf1f4b15ec7751
Víctor Fresno
68db79b0d2bf1f4b15ec7752
Adrian Ghajari
68db79b0d2bf1f4b15ec7753
Guillermo Marco
68db79b0d2bf1f4b15ec7754
Laura Plaza
68db79b0d2bf1f4b15ec7755
Eva Sánchez Salido
BOE-XSUM: Extreme Summarization in Clear Language of Spanish Legal
  Decrees and Notifications
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\'{\i}n 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\%).
BOE-XSUM, a dataset of Spanish legal document summaries, demonstrates that fine-tuned medium-sized LLMs outperform general-purpose models in zero-shot summarization tasks.
BOE-XSUM Boletín Oficial del Estado large language models LLMs fine-tuned models zero-shot setting BERTIN GPT-J 6B DeepSeek-R1