Small language models applied in text summarization task of health-related news to improve public health audit: an experimental case study
The findings highlight the potential of SLMs as tools to reduce information overload, thereby enhancing the effectiveness of the analytical phase of audits and enabling faster preparation of teams for the operational stage.
Authors
- João Paulo Queiroz dos Santos (ORCID: https://orcid.org/0000-0002-9130-7723)
- Luca Pareja Credidio Freire Alves
- METHANIAS COLACO
- Raphael Silva Fontes (ORCID: https://orcid.org/0000-0003-3160-3384)
- Samuel Santana de Almeida (ORCID: https://orcid.org/0000-0001-8662-1673)
- Alysson Guimarães
- Natan Matos
- Ricardo Alexsandro de Medeiros Valentim
- Gabriely Garcia Ferreira de Araújo
- Helder Prado
Institutions
- Universidade Federal de Sergipe (BR)
- Instituto Federal do Rio Grande do Norte (BR)
- Universidade Federal do Rio Grande do Norte (BR)
- Hospital Universitário Onofre Lopes (BR)
- Beijing Advanced Sciences and Innovation Center (CN)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2026-02-05
- DOI
- https://doi.org/10.3389/frai.2026.1708993
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00