Named Entity Recognition based Resume Parser and Summarizer

Abstract

Resumes are unstructured documents and the language used in the resume will be ambiguous and variable. Screening documents in large amounts is a difficult operation, and recruiters and hiring managers waste a lot of time going through each and every CV of the candidates. The extraction of valuable information from a resume is difficult due to this variability. It necessitates an urgent need to comprehend the context in which words are used. Using spaCy, a powerful natural language processing (NLP) package, this study suggests a machine learning method to phrase matching in resumes, focused on the extraction of specific entities. Like a human recruiter, it can examine and extract comprehensive information from resumes. While parsing, it maintains track of the terms in order to classify people. Specific Important entities are extracted from the resume document and then it is stored for later classification. Document ranking is processed and the final rankings are depicted in descending order with respect to the score of the resume in order to make it easy for the recruiters to shortlist the resumes quickly and efficiently. Recruiters may pick the needed applicants based on the scores rather than sifting through stacks of resumes from unqualified prospects.