The development of Features, Events, and Processes (FEPs) and scenarios, which consider the longterm evolution of repository, is underway, along with the construction of input data and a model database for the adaptive process-based total system performance assessment framework, APro. PAPiRUS serves as an integrated information processing platform, enabling users to seamlessly access, search, and extract essential information. To enhance data usability, it is crucial to establish well-structured metadata for each dataset. Regarding FEPs, individual FEPs consist of extensive text-based data and sets of other short textual data. To enhance the searchability of these FEPs, precise keywords must be assigned to each FEP. For user convenience, the PAPiRUS FEP database contains several FEPs not only the long-term evolution FEPs developed by KAERI but also thousands of FEPs form the databases such as NEA PFEPs and Posiva FEPs. Generating keywords for thousands of FEPs proves to be a labor-intensive task. Consequently, this study explores natural language processing techniques for keyword analysis to boost the productivity of the keyword generation process. Specifically, we employ Generative Pretrained Transformer (GPT) models for keyword extraction. Our test results for keyword extraction demonstrate that, although not flawless, providing suitable prompts yields sufficiently useful keyword sets. We identified several optimal prompts and developed an Excel-based program to derive keywords from the existing FEP database using these prompts. By using the outcomes of this study, initial versions of keyword sets for thousands of FEPs can be rapidly produced and subsequently refined through expert review and editing. The generated keywords will serve as metadata within PAPiRUS.