Resume Ranking and Shortlisting with DistilBERT and XLM

Title

Resume Ranking and Shortlisting with DistilBERT and XLM

Creator

Mukherjee A.; Umme Salma M.

Description

The research presented in this paper offers a solution to the time-consuming task of manual recruitment process in the field of human resources (HR). Screening resumes is a challenging and crucial responsibility for HR personnel. A single job opening can attract hundreds of applications. HR employees invest additional time in the candidate selection process to identify the most suitable candidate for the position. Shortlisting the best candidates and selecting the appropriate individual for the job can be difficult and time-consuming. The proposed study aims to streamline the process by identifying candidates who closely match the job requirements based on the skills listed in their resumes. Since it is an automated process, the candidate's individual preferences and soft skills remain unaffected by the hiring process. We leverage advanced Natural Language Processing (NLP) models to improve the recruitment process. Specifically, our emphasis lies in the utilization of the distilBERT model and the XLM (Crosslingual Language Model). This paper explores the application of these two models in taking hundreds of resumes for the job as input and providing the ranked resumes fit for the job as output. To refine our approach further, two types of metrics for resume ranking, such as Cosine similarity score and Spatial Euclidean distance, are used, and the results are compared. Intriguingly, distilBERT and XLM result in different sets of top ten ranked resumes, highlighting the nuanced variations in their ranking approaches. 2024 IEEE.

Source

Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 301-304.

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Automatic Recruitment Process; distilBERT; Natural Language Processing; Resume Ranking; XLM

Coverage

Mukherjee A., Christ Deemed to Be University, Department of Statistics and Data Science, Karnataka, India; Umme Salma M., Christ Deemed to Be University, Department of Statistics and Data Science, Karnataka, India

Rights

Restricted Access

Relation

ISBN: 979-835038328-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICWITE59797.2024.10502523

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192509208&doi=10.1109%2fICWITE59797.2024.10502523&partnerID=40&md5=fa9c0274f4ddc197db5682bb6efea208