CitiLink: Enhancing Municipal Transparency and Citizen Engagement through Searchable Meeting Minutes
- URL: http://arxiv.org/abs/2601.18374v1
- Date: Mon, 26 Jan 2026 11:26:57 GMT
- Title: CitiLink: Enhancing Municipal Transparency and Citizen Engagement through Searchable Meeting Minutes
- Abstract summary: We present CitiLink, a platform designed to transform unstructured municipal meeting minutes into structured and searchable data.<n>The developed system was built over a collection of 120 minutes made available by six Portuguese municipalities.
- Score: 4.531066330197079
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: City council minutes are typically lengthy and formal documents with a bureaucratic writing style. Although publicly available, their structure often makes it difficult for citizens or journalists to efficiently find information. In this demo, we present CitiLink, a platform designed to transform unstructured municipal meeting minutes into structured and searchable data, demonstrating how NLP and IR can enhance the accessibility and transparency of local government. The system employs LLMs to extract metadata, discussed subjects, and voting outcomes, which are then indexed in a database to support full-text search with BM25 ranking and faceted filtering through a user-friendly interface. The developed system was built over a collection of 120 minutes made available by six Portuguese municipalities. To assess its usability, CitiLink was tested through guided sessions with municipal personnel, providing insights into how real users interact with the system. In addition, we evaluated Gemini's performance in extracting relevant information from the minutes, highlighting its effectiveness in data extraction.
Related papers
- Compass: Navigating Global Marine Lead Data Integration through Expert-Guided LLM Agent [55.77954024801206]
Marine lead (Pb) and its isotopes are critical tracers for ocean circulation and anthropogenic pollution.<n>Manual extraction is unscalable, while general-purpose Large Language Models (LLMs) lack the necessary domain-specific knowledge.<n>We introduce an expert-guided adaptation approach that enables LLMs to perform rigorous scientific data extraction without fine-tuning.
arXiv Detail & Related papers (2026-05-28T14:06:25Z) - DreamKG: A KG-Augmented Conversational System for People Experiencing Homelessness [25.758279348968625]
People experiencing homelessness face substantial barriers to accessing timely, accurate information about community services.<n>DreamKG addresses this through a knowledge graph-augmented conversational system that grounds responses in verified, up-to-date data.<n>Preliminary evaluation shows 59% superiority over Google Search AI on relevant queries and 84% rejection of irrelevant queries.
arXiv Detail & Related papers (2026-04-13T16:38:36Z) - CitiLink-Summ: Summarization of Discussion Subjects in European Portuguese Municipal Meeting Minutes [5.13142135666505]
We present CitiLink-Summ, a new corpus of European Portuguese municipal meeting minutes.<n>This dataset comprises 100 documents and 2,322 manually hand-written summaries, each corresponding to a distinct discussion subject.<n>We employ state-of-the-art generative models (e.g., BART, PRIMERA), as well as large language models (LLMs), evaluated with both lexical and semantic metrics such as ROUGE, BLEU, METEOR, and BERTScore.
arXiv Detail & Related papers (2026-02-18T17:03:07Z) - CitiLink-Minutes: A Multilayer Annotated Dataset of Municipal Meeting Minutes [4.992710184213374]
CitiLink-Minutes is an annotated dataset of 120 European Portuguese municipal meeting minutes from six municipalities.<n>The dataset contains over one million tokens, with all personal identifiers de-identified.
arXiv Detail & Related papers (2026-02-12T16:22:55Z) - NLP for Local Governance Meeting Records: A Focus Article on Tasks, Datasets, Metrics and Benchmark [4.320509359331248]
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.<n>These documents are often dense, bureaucratic, and highly heterogeneous across municipalities, exhibiting significant variation in language, terminology, structure, and overall organization.<n>To address these challenges, computational methods can be employed to structure and interpret such complex documents.<n>In this focus article, we review foundational NLP tasks that support the structuring of local governance meeting documents.
arXiv Detail & Related papers (2026-02-08T23:45:17Z) - VotIE: Information Extraction from Meeting Minutes [5.842543771456586]
We introduce VotIE, a new information extraction task aimed at identifying structured voting events in narrative deliberative records.<n>We establish the first benchmark for this task using Portuguese municipal minutes, building on the recently introduced CitiLink corpus.<n>Under standard in-domain evaluation, fine-tuned encoders, specifically XLM-R-CRF, achieve the strongest performance, reaching 93.2% macro F1, outperforming generative approaches.
arXiv Detail & Related papers (2026-01-07T15:06:53Z) - UrbanScore: A Real-Time Personalised Liveability Analytics Platform [0.0]
UrbanScore is a real-time web platform that computes a personalised liveability score for any urban address.<n>System fuses five data streams: address geocoding via Nominatim, (ii) facility extraction from OpenStreetMap through Overpass QL, (iii) segment-level traffic metrics from TomTom Flow v10, (iv) hourly air-quality readings from OpenWeatherMap, and (v) user-declared preference profiles.
arXiv Detail & Related papers (2025-07-16T08:53:11Z) - GRASP: Municipal Budget AI Chatbots for Enhancing Civic Engagement [0.4916484600313162]
We propose GRASP, a custom AI framework which stands for Generation with Retrieval and Action System for Prompts.<n> GRASP provides more truthful and grounded responses to user budget queries than traditional information retrieval systems.<n>During testing, we found that GRASP provided precise and accurate responses for local municipal budget queries 78% of the time.
arXiv Detail & Related papers (2025-03-30T03:46:06Z) - PUBLICSPEAK: Hearing the Public with a Probabilistic Framework in Local Government [0.01499944454332829]
We propose PUBLICSPEAK, a probabilistic framework which can discover public remarks in local government meetings.<n>We evaluate our approach on a novel dataset of local government meetings and find that PUBLICSPEAK improves over state-of-the-art by 10% on average, and by up to 40%.
arXiv Detail & Related papers (2025-03-14T17:04:36Z) - Uchaguzi-2022: A Dataset of Citizen Reports on the 2022 Kenyan Election [49.35115948941981]
We present Uchaguzi-2022, a dataset of 14k categorized and geotagged citizen reports related to the 2022 Kenyan General Election.<n>We use this dataset to investigate whether language models can assist in scalably categorizing and geotagging reports, thus highlighting its potential application in the AI for Social Good space.
arXiv Detail & Related papers (2024-12-17T17:08:35Z) - KamerRaad: Enhancing Information Retrieval in Belgian National Politics through Hierarchical Summarization and Conversational Interfaces [55.00702535694059]
KamerRaad is an AI tool that leverages large language models to help citizens interactively engage with Belgian political information.
The tool extracts and concisely summarizes key excerpts from parliamentary proceedings, followed by the potential for interaction based on generative AI.
arXiv Detail & Related papers (2024-04-22T15:01:39Z) - Leveraging Large Language Models for Topic Classification in the Domain
of Public Affairs [65.9077733300329]
Large Language Models (LLMs) have the potential to greatly enhance the analysis of public affairs documents.
LLMs can be of great use to process domain-specific documents, such as those in the domain of public affairs.
arXiv Detail & Related papers (2023-06-05T13:35:01Z) - Explaining Patterns in Data with Language Models via Interpretable
Autoprompting [143.4162028260874]
We introduce interpretable autoprompting (iPrompt), an algorithm that generates a natural-language string explaining the data.
iPrompt can yield meaningful insights by accurately finding groundtruth dataset descriptions.
Experiments with an fMRI dataset show the potential for iPrompt to aid in scientific discovery.
arXiv Detail & Related papers (2022-10-04T18:32:14Z) - Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings [44.4879068879732]
This paper presents a complete pipeline for resolving ambiguities during the data association.
Its core is a robust self-tuning data association that adapts the search area depending on the entropy of the measurements.
We evaluate our method on real data from urban and rural scenarios around the city of Karlsruhe in Germany.
arXiv Detail & Related papers (2022-07-28T12:29:39Z) - Automatic Speech Recognition Datasets in Cantonese Language: A Survey
and a New Dataset [85.52036362232688]
Our dataset consists of 73.6 hours of clean read speech paired with transcripts, collected from Cantonese audiobooks from Hong Kong.
It combines philosophy, politics, education, culture, lifestyle and family domains, covering a wide range of topics.
We create a powerful and robust Cantonese ASR model by applying multi-dataset learning on MDCC and Common Voice zh-HK.
arXiv Detail & Related papers (2022-01-07T12:09:15Z) - Real-time Outdoor Localization Using Radio Maps: A Deep Learning
Approach [59.17191114000146]
LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task.
We show that LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps.
arXiv Detail & Related papers (2021-06-23T17:27:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.