CitiLink-Summ: Summarization of Discussion Subjects in European Portuguese Municipal Meeting Minutes

2026-02-18

AI Summary

This study addresses the lack of publicly available automatic summarization systems for European Portuguese municipal council minutes—a domain characterized by lengthy, complex administrative texts and scarce high-quality annotated data due to its low-resource setting. To bridge this gap, the authors introduce CitiLink-Summ, the first summarization dataset for European Portuguese council minutes, comprising 100 meeting records paired with 2,322 human-written thematic summaries. They establish strong baselines using state-of-the-art generative models, including BART and PRIMERA, as well as large language models (LLMs). Comprehensive evaluation via ROUGE, BLEU, METEOR, and BERTScore demonstrates the dataset’s utility, offering both a foundational resource and benchmark for advancing natural language processing in complex governmental discourse under low-resource conditions.

Technology Category

• Natural Language Processing: Summarization
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• municipal meeting minutes
• automatic summarization
• low-resource languages
• discussion subjects
• dataset scarcity

Innovation

• municipal meeting summarization
• low-resource language
• manual summary corpus
• European Portuguese
• automatic summarization benchmark