MiNER: A Two-Stage Pipeline for Metadata Extraction from Municipal Meeting Minutes

TL;DR

This paper introduces a two-stage pipeline utilizing question answering and transformer-based models for extracting metadata from heterogeneous municipal meeting minutes, establishing a new benchmark for this task.

Contribution

It presents the first benchmark for metadata extraction from municipal minutes, combining QA and fine-grained entity recognition with evaluation of LLMs' performance, cost, and carbon footprint.

Findings

Strong in-domain performance surpassing larger general-purpose LLMs

Reduced cross-municipality generalization due to variability in records

Benchmarking of open-weight and closed-weight LLMs for this task

Abstract

Municipal meeting minutes are official documents of local governance, exhibiting 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 easy to extract 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 and enhanced through deslexicalization. To evaluate our proposed pipeline, we benchmark both open-weight (Phi) and closed-weight (Gemini) LLMs, assessing predictive performance, inference cost, and carbon footprint. Our results demonstrate strong in-domain performance, better than larger general-purpose LLMs. However, cross-municipality evaluation reveals reduced generalization reflecting the variability and linguistic complexity of municipal records. This work establishes the first benchmark for metadata extraction from municipal meeting minutes, providing a solid foundation for future research in this domain.