On the use of summarization and transformer architectures for profiling résumés. (1st December 2021) - Bondielli, Alessandro
Abstract: Profiling professional figures is becoming more and more crucial, as companies and recruiters face the challenges of Industry 4.0. On the one hand, demand for specific knowledge in professional figures is rising. On the other hand, workers try to broaden the spectrum of their skills in order to remain appealing in the job market. Therefore, research related to these topics is receiving more and more attention. In this paper, we propose a methodology to profile résumés based on summarization and transformer architectures for generating résumé embeddings and on hierarchical clustering algorithms for grouping these embeddings. We evaluate different strategies and show that our approach achieves promising results on a public domain dataset containing 1202 résumés. Highlights: Methodology to automatically profile résumés of job candidates. Text summarization of résumés improves performance. Transformer Architectures for representing résumés as real vectors. Profiles generated by applying hierarchical clustering to résumé embeddings. Profile hierarchies useful to satisfy job offers.