AUTOMATIC ABSTRACTIVE SUMMARIZATION OF CURRICULUM VITAE USING S-BERT AND T5

The rapid advancement of technological disruption has catalyzed significant innovations in human resource management, particularly through the widespread adoption of automated applicant screening systems such as Applicant Tracking Systems (ATS). However, these systems often fail to identify potential candidates due to poorly formatted Curriculum Vitae (CV) or missing important keywords, resulting in many applicants being eliminated in the early stages of selection. This research aims to develop an automatic CV summarization system by utilizing Natural Language Processing (NLP) technology. This research uses a combination of Sentence-BERT (SBERT) algorithm for information extraction and Text-to-Text Transfer Transformer (T5) for text generation. The K-Fold Cross Validation method with k = 3 was used in the model performance evaluation, in accordance with the limited computing resources. Experimental results show that the SBERT model is able to extract important information with high accuracy (F1-score of 0.8866), while the T5 model is able to generate informative summaries with a ROUGE-1 score of 0.8680. The combination of SBERT in producing important information extraction from CV and T5 that produces an abstractive summary shows good results with ROUGE-1 scores of 0.5497, ROUGE-2 of 0.3537, and ROUGE-L of 0.4334. This system is able to produce CV summaries that make it easier for companies to select job applicants according to the criteria and increase the chances of applicants to pass the initial selection stage