Browsing by Author "Gana, Bady"
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Publication Large scale summarization using ensemble prompts and in context learning approaches(2025) Leiva-Araos, Andrés; Gana, Bady; Allende-Cid, Héctor; García, José; Jyoti Saikia, ManobThe field of Information Assurance (IA) and Cybersecurity has seen substantial evolution, driven by advancements in technology and the increasing sophistication of threats in the digital age. This study employs Large Language Models (LLMs), as well as other advanced NLP techniques, to conduct a comprehensive analysis of literature from 1967 to 2024. By analyzing a corpus of more than 62,000 documents extracted from Scopus, our approach involves a comprehensive methodology that includes two main phases: topic detection using BERTopic and automatic summarization with LLMs across various periods (annual and decades). By designing targeted queries to extract relevant papers, analyzing textual data, and applying advanced prompting techniques for summarization, we integrate computational models to handle large volumes of data. Our results demonstrate that an ensemble of methods (Ev2) outperforms traditional summarization and density-based approaches, with improvements ranging from 16.7% to 29.6% in keyword definition tasks. It generates summaries that outperform in 5 out of the 7 tested metrics while maintaining the logical integrity of bibliographic references. Our results illuminate the shifts in focus within Information Assurance across decades, revealing key breakthroughs and forecasting emerging areas of significance.Publication Leveraging LLMs for Efficient Topic Reviews(2024) Gana, Bady; Leiva-Araos, Andrés; Allende-Cid, Héctor; García, JoséThis paper presents the topic review (TR), a novel semi-automatic framework designed to enhance the efficiency and accuracy of literature reviews. By leveraging the capabilities of large language models (LLMs), TR addresses the inefficiencies and error-proneness of traditional review methods, especially in rapidly evolving fields. The framework significantly improves literature review processes by integrating advanced text mining and machine learning techniques. Through a case study approach, TR offers a step-by-step methodology that begins with query generation and refinement, followed by semi-automated text mining to identify relevant articles. LLMs are then employed to extract and categorize key themes and concepts, facilitating an in-depth literature analysis. This approach demonstrates the transformative potential of natural language processing in literature reviews. With an average similarity of 69.56% between generated and indexed keywords, TR effectively manages the growing volume of scientific publications, providing researchers with robust strategies for complex text synthesis and advancing knowledge in various domains. An expert analysis highlights a positive Fleiss’ Kappa score, underscoring the significance and interpretability of the results.