Segmentation and Summarization for Extracting Information about Information Technology Equipment from Government Procurement Notice - Silva, Erick Correia; Medeiros, Ivo Paixão de; Menezes, Maria Viviane de; Kamikawachi, Dayse Simon Landim (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2024: Anais do XII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2024.244753, 2024)
Government procurement in Brazil employs a bidding process to acquire products and services, involving stages such as the publication of public notices, which are structured documents outlining procurement rules and specifications. For Information Technology (IT) companies, competitive participation in the bidding process includes monitoring opportunities by analyzing data from these notices. This paper applies text segmentation and summarization algorithms to extract data such as product names, prices and quantities from IT procurement notices. Four architectures are proposed: (i) sentence-based segmentation followed by K-means clustering; (ii) section-based segmentation followed by K-means clustering; (iii) sentence-based segmentation followed by BERTimbau clustering; and (iv) section-based segmentation followed by BERTimbau clustering. For all texts clustered as an interest profile, the Large Language Model (LLM) GPT-3.5 is applied in order to summarize and organize the information regarding product names, prices and quantities. Evaluation using real public notices from Federal and State Government Procurement sites shows that BERTimbau significantly outperformed K-means in both sentence and section segmentation tasks.