面向地方治理会议记录的NLP：聚焦任务、数据集、指标和基准
NLP for Local Governance Meeting Records: A Focus Article on Tasks, Datasets, Metrics and Benchmark
AI总结：
本文聚焦地方治理会议记录的NLP任务，探讨文档分割、实体提取和文本摘要等关键技术，旨在提升政府记录的可访问性和解读性。
摘要：
地方治理会议记录是官方文件，以会议纪要或转录形式记录机构会议中提案、讨论和程序性行动的展开情况。尽管通常结构化，但这些文档往往密集、官僚化，并在不同城市之间表现出显著的异质性，语言、术语、结构和整体组织存在显著差异。这种异质性使非专家难以解读，也使智能自动系统难以处理，限制了公共透明度和公民参与。为解决这些挑战，可以采用计算方法对这类复杂文档进行结构化和解读。特别是，自然语言处理（NLP）提供了已建立的方法，可以增强政府记录的可访问性和可解释性。在本文聚焦文章中，我们回顾了支持地方治理会议文档结构化的基础NLP任务。具体而言，我们回顾了三个核心任务：文档分割、领域特定实体提取和自动文本摘要，这些任务对于处理长篇讨论、识别政治人物和个人信息以及生成复杂决策过程的简洁表示至关重要。在回顾这些任务时，我们讨论了方法论方法、评估指标和公开可用的资源，同时突出了领域特定的挑战，如数据稀缺、隐私限制和来源变化。通过综合现有工作，本文提供了NLP如何增强地方治理会议记录结构化和可访问性的结构化概述。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
