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Large scale summarization using ensemble prompts and in context learning approaches.

Andrés Leiva-Araos1,2,3, Bady Gana4, Héctor Allende-Cid4,5,6

  • 1Department of Computing, University of North Florida, Jacksonville, FL, 32224, USA.

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|March 26, 2025
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Summary
This summary is machine-generated.

This study used Large Language Models (LLMs) to analyze cybersecurity literature, revealing key shifts and emerging trends in Information Assurance (IA) over decades. An ensemble method improved keyword definition and summarization accuracy.

Keywords:
Automatic summarizationCybersecurity trendsInformation assuranceLarge language modelsNatural language processing (NLP)Systematic topic review

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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.