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Exploring the Applicability of Large Language Models for Academic Abstract Writing

  • Journal of the Korean Society for Library and Information Science
  • 2025, 59(4), pp.177~198
  • Publisher : 한국문헌정보학회
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : October 20, 2025
  • Accepted : November 10, 2025
  • Published : November 30, 2025

Yumi Kim 1 Seungwon Yang 2 Jongwook Lee 3

1경북대학교 문헌정보학과
2Center for Computation & Technology, Louisiana State University
3경북대학교

Excellent Accredited

ABSTRACT

Since the release of ChatGPT, researchers have shown growing interest in using large language models (LLMs) for academic writing. This study explored the possibilities and limitations of AI use in summarizing the core content of research papers into abstracts. We analyzed 204 papers published in the Journal of the Korean Society for Library and Information Science (2022-2024) by comparing author-written abstracts with those generated by ChatGPT. Quantitative analyses examined similarities among the originals, AI-generated abstracts, full texts, and different prompt types, supplemented by expert perception. Results showed that AI-generated abstracts were semantically close to the originals based on BERT and TF-IDF scores but differed in word choice and expression. Abstracts generated with Korean prompts showed the highest similarity to both the originals and the full texts, indicating that the prompt language affected style and content representation. Experts viewed LLMs as helpful tools for improving clarity and fluency in writing. Overall, the findings suggest the potential of LLMs as collaborative partners in abstract writing.

Citation status

* References for papers published after 2024 are currently being built.

This paper was written with support from the National Research Foundation of Korea.