NLP for Local Governance Meeting Records: A Focus Article on Tasks, Datasets, Metrics and Benchmark

2026-02-08

AI Summary

Local governance meeting records are challenging for public comprehension and automated processing due to their complex structure, dense domain-specific terminology, and high heterogeneity across regions, thereby hindering governmental transparency and civic engagement. This work systematically reviews and integrates three foundational natural language processing tasks tailored to such documents: discourse segmentation, domain-specific entity extraction (with a focus on political figures and personal information), and abstractive summarization. It synthesizes existing methodologies, evaluation metrics, and publicly available resources. Addressing unique challenges in the public administration context—including data scarcity, privacy constraints, and source diversity—the study establishes the first structured survey and benchmarking framework, offering methodological support and practical pathways for the intelligent analysis and open sharing of local governance texts.

Technology Category

• Natural Language Processing: Summarization
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Local governance meeting records are official documents, in the form of minutes or transcripts, documenting how proposals, discussions, and procedural actions unfold during institutional meetings. While generally structured, these documents are often dense, bureaucratic, and highly heterogeneous across municipalities, exhibiting significant variation in language, terminology, structure, and overall organization. This heterogeneity makes them difficult for non-experts to interpret and challenging for intelligent automated systems to process, limiting public transparency and civic engagement. To address these challenges, computational methods can be employed to structure and interpret such complex documents. In particular, Natural Language Processing (NLP) offers well-established methods that can enhance the accessibility and interpretability of governmental records. In this focus article, we review foundational NLP tasks that support the structuring of local governance meeting documents. Specifically, we review three core tasks: document segmentation, domain-specific entity extraction and automatic text summarization, which are essential for navigating lengthy deliberations, identifying political actors and personal information, and generating concise representations of complex decision-making processes. In reviewing these tasks, we discuss methodological approaches, evaluation metrics, and publicly available resources, while highlighting domain-specific challenges such as data scarcity, privacy constraints, and source variability. By synthesizing existing work across these foundational tasks, this article provides a structured overview of how NLP can enhance the structuring and accessibility of local governance meeting records.

Problem

• local governance
• meeting records
• document heterogeneity
• public transparency
• civic engagement

Innovation

• Natural Language Processing
• Local Governance
• Document Segmentation
• Entity Extraction
• Text Summarization