Large scale summarization using ensemble prompts and in context learning approaches

Area of Science:

• Information Assurance
• Cybersecurity
• Natural Language Processing (NLP)

Background:

• The fields of Information Assurance (IA) and Cybersecurity have evolved significantly due to technological advancements and increasingly sophisticated digital threats.
• A comprehensive analysis of IA literature is crucial for understanding its historical development and future trajectory.

Purpose of the Study:

• To conduct a comprehensive analysis of IA and Cybersecurity literature from 1967 to 2024 using advanced NLP techniques.
• To identify shifts in focus, key breakthroughs, and emerging areas within IA over time.
• To evaluate the effectiveness of Large Language Models (LLMs) and ensemble methods in literature analysis and summarization.

Main Methods:

• Analysis of a corpus exceeding 62,000 documents from Scopus (1967-2024).
• Topic detection using BERTopic and automatic summarization with LLMs across annual and decadal periods.
• Application of targeted queries, textual data analysis, and advanced prompting techniques for summarization.

Main Results:

• An ensemble of methods (Ev2) demonstrated superior performance over traditional summarization and density-based approaches.
• Improvements ranged from 16.7% to 29.6% in keyword definition tasks.
• The ensemble method outperformed in 5 out of 7 tested metrics for summary generation, maintaining bibliographic integrity.

Conclusions:

• The study illuminates the evolving focus within Information Assurance across decades.
• Key breakthroughs and emerging areas of significance in IA and Cybersecurity are identified.
• LLMs and ensemble methods offer powerful computational tools for large-scale literature analysis in specialized scientific fields.