A Systematic Literature Review (SLR) On The Beginning of Resume Parsing in HR Recruitment Process & SMART Advancements in Chronological Order
Year: 2021
Type: preprint
Study design: Systematic Review
Abstract: Talent acquisition, also known as recruitment, is definitely amongst one of the most difficult decisions that an organization has to take. The workforce is the most crucial pillar of any organization and surely a deciding factor for its fate. Each organization/company/firm has a dedicated department in place for completely managing an employee’s life cycle starting from recruitment till termination, known as the Human Resource (HR) department. National Cash Register Co. was the first company ever to include an HR department in 1900 for resolving conflicts among existing employees. Since then, the world of recruitment has grown rapidly with a lot of advancements to fast-track the hiring process. Realising the importance of hiring fitting and apt employees, HR units around the world have undergone substantial changes from traditional recruitment methodologies to network recruitment and finally to smart hybrid recruitment strategies for efficient hiring with a significantly less human workload. The purpose of this Systematic Literature Review (SLR) article is to shed light on all the advancements in the hiring process from a technical perspective. The article follows the defined format and all appropriate guidelines of an SLR and would provide an extensive study of various Machine Learning (ML) and Deep Learning (DL) approaches to facilitate hiring. The main emphasis has been given to “Resume/CV Parsing” as an enhancer of fast-track hiring. The SLR would be instrumental in providing various fast-track approaches to go with for resume parsing, the scope of improvement and also focus on various challenges/ethical considerations to keep in mind while automating the hiring process.
Source: Research Square
Authors: Aakankshu Rawat, Siddharth Malik, Seema Rawat, Deepak Kumar, Praveen Kumar
Institution: Amity University
Language: English
Topic: AI and HR Technologies
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3180088549