Leveraging Large Language Models (LLMs) for Automated Key Point Extraction in Qualitative Data Analysis

Abstract

This paper introduces a novel approach to qualitative data analysis by leveraging large language models (LLMs) for the automatic generation of key points from unstructured textual data. Traditional qualitative analysis often requires significant manual effort to identify and summarize key insights from large datasets. By employing LLMs, the proposed method automates this process, providing researchers with a powerful tool to efficiently extract and organize critical themes and patterns. The paper demonstrates the effectiveness of this approach through case studies in various domains, highlighting its potential to enhance the accuracy and scalability of qualitative research. The results indicate that LLMs can significantly reduce the time and effort required for key point generation while maintaining high analytical quality.