Evelin Freire Amorim

Abstract
Municipal meeting minutes are official documents of local governance that exhibit heterogeneous formats and writing styles. Effective information retrieval (IR) requires identifying metadata such as meeting number, date, location, participants, and start/end times, elements that are rarely standardized or easily extracted automatically. Existing named entity recognition (NER) models are ill-suited to this task, as they are not adapted to such domain-specific categories. In this paper, we propose a two-stage pipeline for metadata extraction from municipal minutes. First, a question-answering (QA) model identifies the opening and closing text segments containing metadata. Transformer-based models (BERTimbau and XLM-RoBERTa with and without a CRF layer) are then applied for fine-grained entity extraction, with deslexicalization explored as an additional modeling strategy. We benchmark the pipeline against open and closed-weight LLMs (Phi and Gemini), considering performance, inference cost, and carbon footprint. Our results demonstrate strong in-domain performance, outperforming the evaluated LLMs. Differences observed in cross-municipality evaluation highlight the linguistic diversity and structural variation across municipal records, underscoring the challenges of generalization in this domain and motivating future research in metadata extraction from municipal minutes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
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 four complementary dimensions: (1) personal information, (2) metadata, (3) subjects of discussion, and (4) 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.

Abstract

Abstract
The definition of rigorous and well-structured annotation schemes is a key element in the advancement of Natural Language Processing (NLP). This paper aims to compare the performance of a general-purpose annotation scheme - Text2Story, based on the ISO 24617-1 standard-with that of a domain-specific scheme - i2b2 - in the context of clinical narrative annotation; and to assess the feasibility of harmonizing ISO 24617-1, originally designed for general-domain applications, with a specialized extension tailored to the medical domain. Based on the results of this comparative analysis, we present Med2Story, a medical-specific extension of ISO 24617-1 developed to address the particularities of clinical text annotation.

Abstract