BOE-XSUM：西班牙法律法令與通知的極簡清晰摘要

摘要

在資訊過載的當下，簡明扼要地總結長篇文件的能力日益重要，然而針對西班牙語文件，尤其是法律領域的此類摘要卻顯著缺乏。本研究介紹了BOE-XSUM，這是一個精心整理的數據集，包含3,648份來自西班牙《國家官方公報》（Boletín Oficial del Estado, BOE）文件的簡明易懂摘要。數據集中的每一條目均包含一份簡短摘要、原文及其文件類型標籤。我們評估了在BOE-XSUM上微調的中等規模大型語言模型（LLMs）的性能，並將其與零樣本設置下的通用生成模型進行了比較。結果顯示，經過微調的模型顯著優於非專業化模型。值得注意的是，表現最佳的模型——BERTIN GPT-J 6B（32位精度）——相較於頂尖的零樣本模型DeepSeek-R1，性能提升了24%（準確率分別為41.6%對33.5%）。

English

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：西班牙法律法令與通知的極簡清晰摘要

BOE-XSUM: Extreme Summarization in Clear Language of Spanish Legal Decrees and Notifications

摘要

Support