VotIE: Information Extraction from Meeting Minutes

TL;DR

VotIE introduces a new task and benchmark for extracting structured voting information from heterogeneous municipal meeting minutes, highlighting the strengths and limitations of fine-tuned encoders versus large language models.

Contribution

The paper establishes the first benchmark for voting information extraction from municipal minutes and compares the effectiveness of different models, including fine-tuned encoders and LLMs.

Findings

Fine-tuned XLM-R-CRF achieves 93.2% macro F1 in in-domain evaluation.

Few-shot LLMs show better robustness in cross-municipality transfer.

Generative models are computationally costly, limiting practical deployment.

Abstract

Municipal meeting minutes record key decisions in local democratic processes. Unlike parliamentary proceedings, which typically adhere to standardized formats, they encode voting outcomes in highly heterogeneous, free-form narrative text that varies widely across municipalities, posing significant challenges for automated extraction. In this paper, we introduce VotIE (Voting Information Extraction), a new information extraction task aimed at identifying structured voting events in narrative deliberative records, and establish the first benchmark for this task using Portuguese municipal minutes, building on the recently introduced CitiLink corpus. Our experiments yield two key findings. First, under standard in-domain evaluation, fine-tuned encoders, specifically XLM-R-CRF, achieve the strongest performance, reaching 93.2\% macro F1, outperforming generative approaches. Second, in a cross-municipality setting that evaluates transfer to unseen administrative contexts, these models suffer substantial performance degradation, whereas few-shot LLMs demonstrate greater robustness, with significantly smaller declines in performance. Despite this generalization advantage, the high computational cost of generative models currently constrains their practicality. As a result, lightweight fine-tuned encoders remain a more practical option for large-scale, real-world deployment. To support reproducible research in administrative NLP, we publicly release our benchmark, trained models, and evaluation framework.