@inproceedings{aguiar-etal-2026-geological,
    title = "Geological Text Summarization Using Generative Large Language Models",
    author = "Aguiar, Matheus Stein de  and
      Nunes, Rafael Oleques  and
      Balreira, Dennis Giovani",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-dnd/2026.propor-1.11/",
    pages = "111--119",
    ISBN = "979-8-89176-387-6",
    abstract = "Large generative language models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks. However, the geological domain presents unique challenges for NLP due to its specialized language, which is full of technical terms. Therefore, pre-trained language models on generic corpora may not be suitable for performing geological domain-specific tasks. This article compares several models to identify those with the best performance in the Portuguese geological domain for a text summarization task. We applied the models to a Revista Geologia USP dataset. The dataset consists of abstracts of scientific texts and their respective titles, which we aim for the models to approximate with the summarization task. We tested the models in various scenarios, providing examples or not, and at two temperature levels. We then evaluated the models' performance using quantitative metrics and a brief qualitative analysis comparing the titles proposed by the models with the original title. The results show that the Gemma3:27b model was better in some scenarios, while the Llama3:8b model performed best in others."
}