|International Journal of Computer Applications
|Foundation of Computer Science (FCS), NY, USA
|Volume 186 - Number 6
|Year of Publication: 2024
|Authors: Akshata Upadhye
Akshata Upadhye . A Comprehensive Study of Resume Summarization using Large Language Models. International Journal of Computer Applications. 186, 6 ( Jan 2024), 33-37. DOI=10.5120/ijca2024923401
Due to a large number of applications received for a job posting, the recruiters and hiring teams can afford to spend very less time reviewing each resume. Due to the time constraint, it could be very helpful to the recruiters and the hiring teams if the key information from a resume could be summarized to provide a quick overview of the candidate’s skills and experiences for initial screening. Therefore, this research focuses on exploring resume summarization through the utilization of various Language Models. This study explores the efficiency of various models like the BERT, T5 and BART for extractive and abstractive summarization in comprehensively summarizing diverse resumes. The research investigates the potential of LLMs in capturing important information, skills, and experiences, aiming to enhance the efficiency of the hiring process. By leveraging the power of these language models, the goal of this research is to contribute to the evolution of resume summarization techniques, offering a more context-aware approach for recruiters and the hiring teams.