Resume Shortlisting and Ranking with Transformers



The study shown in this paper helps the human resource domain eliminate the time-consuming recruitment process task. Screening resume is the most critical and challenging task for human resource personnel. Natural Language Processing (NLP) techniques are the computer’s ability to understand spoken/written language. Now a day’s, online recruitment platform is more vigorous along with consultancies. A single job opening will get hundreds of applications. To discover the finest candidate for the position, Human Resource (HR) employees devote extra time to the candidate selection process. Most of the time, shortlisting the best fit for the job is time-consuming and finding an apt person is hectic. The proposed study helps to shortlist the candidates with a better match for the job based on the skills provided in the resume. As it is an automated process, the candidate’s personalized favor and soft skills are not affected by the hiring process.  The Sentence-BERT (SBERT) network is a Siamese and triplet network-based variant of the Bidirectional Encoder Representations from Transformers (BERT) architecture, which may generate semantically significant sentence embeddings. An end-to-end tool for the HR domain, which takes hundreds of resumes along with required skills for the job as input and provides the better-ranked candidate fit for the job as output. The SBERT is compared with BERT and proved that it is superior to BERT.

Keywords: Natural Language Processing, Sentence-BERT, Automatic Recruitment Process, Sentence Embedding


Journal Name:  Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering


Vinaya James

Akshay Kulkarni

Rashmi Agarwal

Leave a Reply

Your email address will not be published. Required fields are marked *