Large scale summarization using ensemble prompts and in context learning approaches
Abstract The 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 condu...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-94551-8 |
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| author | Andrés Leiva-Araos Bady Gana Héctor Allende-Cid José García Manob Jyoti Saikia |
| author_facet | Andrés Leiva-Araos Bady Gana Héctor Allende-Cid José García Manob Jyoti Saikia |
| author_sort | Andrés Leiva-Araos |
| collection | DOAJ |
| description | Abstract The 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. |
| format | Article |
| id | doaj-art-7f113ceaffae4734a572bcdc94c85e75 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7f113ceaffae4734a572bcdc94c85e752025-08-20T02:10:16ZengNature PortfolioScientific Reports2045-23222025-03-0115112110.1038/s41598-025-94551-8Large scale summarization using ensemble prompts and in context learning approachesAndrés Leiva-Araos0Bady Gana1Héctor Allende-Cid2José García3Manob Jyoti Saikia4Department of Computing, University of North FloridaEscuela de Ingeniería Informática, Pontificia Universidad Católica de ValparaísoEscuela de Ingeniería Informática, Pontificia Universidad Católica de ValparaísoEscuela de Ingeniería en Construcción y Transporte, Pontificia Universidad Católica de ValparaísoDepartment of Electrical Engineering, University of North FloridaAbstract The 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.https://doi.org/10.1038/s41598-025-94551-8Information assuranceCybersecurity trendsSystematic topic reviewLarge language modelsNatural language processing (NLP)Automatic summarization |
| spellingShingle | Andrés Leiva-Araos Bady Gana Héctor Allende-Cid José García Manob Jyoti Saikia Large scale summarization using ensemble prompts and in context learning approaches Scientific Reports Information assurance Cybersecurity trends Systematic topic review Large language models Natural language processing (NLP) Automatic summarization |
| title | Large scale summarization using ensemble prompts and in context learning approaches |
| title_full | Large scale summarization using ensemble prompts and in context learning approaches |
| title_fullStr | Large scale summarization using ensemble prompts and in context learning approaches |
| title_full_unstemmed | Large scale summarization using ensemble prompts and in context learning approaches |
| title_short | Large scale summarization using ensemble prompts and in context learning approaches |
| title_sort | large scale summarization using ensemble prompts and in context learning approaches |
| topic | Information assurance Cybersecurity trends Systematic topic review Large language models Natural language processing (NLP) Automatic summarization |
| url | https://doi.org/10.1038/s41598-025-94551-8 |
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