LLM Memory & RAG 相关度: 7/10

CitiLink-Minutes: A Multilayer Annotated Dataset of Municipal Meeting Minutes

Ricardo Campos, Ana Filipa Pacheco, Ana Luísa Fernandes, Inês Cantante, Rute Rebouças, Luís Filipe Cunha, José Miguel Isidro, José Pedro Evans, Miguel Marques, Rodrigo Batista, Evelin Amorim, Alípio Jorge, Nuno Guimarães, Sérgio Nunes, António Leal, Purificação Silvano
arXiv: 2602.12137v1 发布: 2026-02-12 更新: 2026-02-12

AI 摘要

CitiLink-Minutes是一个欧洲葡萄牙市政会议记录的多层注释数据集,旨在促进NLP和IR在该领域的应用。

主要贡献

  • 创建了包含超过一百万个tokens的多层注释市政会议记录数据集
  • 提供了元数据、讨论主题和投票结果三个维度的注释
  • 发布了基于数据集的元数据提取、主题分类和投票标签的基线结果

方法论

人工对120份会议记录进行注释,包括元数据、讨论主题和投票结果,并由语言学家进行校正和审核。

原文摘要

City councils play a crucial role in local governance, directly influencing citizens' daily lives through decisions made during municipal meetings. These deliberations are formally documented in meeting minutes, which serve as official records of discussions, decisions, and voting outcomes. Despite their importance, municipal meeting records have received little attention in Information Retrieval (IR) and Natural Language Processing (NLP), largely due to the lack of annotated datasets, which ultimately limit the development of computational models. To address this gap, we introduce CitiLink-Minutes, a multilayer dataset of 120 European Portuguese municipal meeting minutes from six municipalities. Unlike prior annotated datasets of parliamentary or video records, CitiLink-Minutes provides multilayer annotations and structured linkage of official written minutes. The dataset contains over one million tokens, with all personal identifiers de-identified. Each minute was manually annotated by two trained annotators and curated by an experienced linguist across three complementary dimensions: (1) metadata, (2) subjects of discussion, and (3) voting outcomes, totaling over 38,000 individual annotations. Released under FAIR principles and accompanied by baseline results on metadata extraction, topic classification, and vote labeling, CitiLink-Minutes demonstrates its potential for downstream NLP and IR tasks, while promoting transparent access to municipal decisions.

标签

NLP IR Dataset Municipal Meeting Annotation

arXiv 分类

cs.CL