构建首个葡萄牙语市政会议纪要摘要数据集,助力政务文本自动化提炼
CitiLink-Summ: Summarization of Discussion Subjects in European Portuguese Municipal Meeting Minutes
- 基于100篇会议纪要,人工标注2322个议题摘要,建立首个葡萄牙语政务摘要数据集
- 使用BART、PRIMERA等模型在该数据集上取得最佳ROUGE-L得分0.432,验证方法可行性
- 为低资源语言政务文本处理提供基准,适合研究公共治理与NLP交叉的学者
市政会议纪要是记录地方政府讨论与决策的正式文档,但内容冗长密集,公众难以理解。自动摘要可生成每个议题的简洁总结,提升信息可及性。然而,针对市政会议纪要中议题摘要的研究仍处于空白,尤其在低资源语言中面临更大挑战,主要瓶颈是缺乏高质量人工标注的摘要数据集。本文提出CitiLink-Summ,一个包含100篇欧洲葡萄牙语市政会议纪要和2,322条手动撰写议题摘要的新语料库。基于此数据集,我们采用BART、PRIMERA等先进生成模型及大语言模型(LLMs),通过ROUGE、BLEU、METEOR、BERTScore等词汇与语义指标建立该领域基线性能。实验结果表明,最优模型在ROUGE-L上达到0.432,首次为欧洲葡萄牙语市政文本摘要提供基准,推动复杂行政文本的NLP研究发展。
原文摘要 · Abstract (English)
Municipal meeting minutes are formal records documenting the discussions and decisions of local government, yet their content is often lengthy, dense, and difficult for citizens to navigate. Automatic summarization can help address this challenge by producing concise summaries for each discussion subject. Despite its potential, research on summarizing discussion subjects in municipal meeting minutes remains largely unexplored, especially in low-resource languages, where the inherent complexity of these documents adds further challenges. A major bottleneck is the scarcity of datasets containing high-quality, manually crafted summaries, which limits the development and evaluation of effective summarization models for this domain. In this paper, we present CitiLink-Summ, a new corpus of European Portuguese municipal meeting minutes, comprising 100 documents and 2,322 manually hand-written summaries, each corresponding to a distinct discussion subject. Leveraging this dataset, we establish baseline results for automatic summarization in this domain, employing state-of-the-art generative models (e.g., BART, PRIMERA) as well as large language models (LLMs), evaluated with both lexical and semantic metrics such as ROUGE, BLEU, METEOR, and BERTScore. CitiLink-Summ provides the first benchmark for municipal-domain summarization in European Portuguese, offering a valuable resource for advancing NLP research on complex administrative texts.
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