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.
Abstract (translated)
地方治理会议记录是官方文件，以会议纪要或实录的形式记载了提案、讨论和程序性行动在机构会议中的展开过程。尽管这些文档通常具有结构化的特征，但它们往往内容密集、官僚化，并且跨地区表现出语言、术语、结构以及整体组织上的高度异质性，这使得非专业人士难以解读，并给智能自动化系统处理带来挑战，从而限制了公共透明度和市民参与度。为了应对这些问题，可以采用计算方法来整理并解析这些复杂的文档。具体而言，自然语言处理（NLP）提供了一套成熟的方法，能够增强政府记录的可访问性和解释性。 本文聚焦于介绍支持地方治理会议文件结构化的基础NLP任务。特别地，我们将回顾三个核心任务：文档分割、领域特定实体抽取和自动文本摘要。这些任务对于梳理冗长讨论、识别政治角色和个人信息以及生成复杂决策过程的简洁表示至关重要。在探讨这些任务时，本文将涉及方法论途径、评估指标以及公开可用资源，并突出数据稀缺性、隐私限制及来源变化等领域的特定挑战。 通过综合现有工作的基础任务，本文为如何利用NLP提升地方治理会议记录的结构化与可访问性提供了一个有条理的概述。
URL
https://arxiv.org/abs/2602.08162
