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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM26647

RESUME SUMMARIZER AND JOB DESCRIPTION MATCHER USING NATURAL LANGUAGE PROCESSING AND SPACY

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM26647

Volume:

7 |

11 |

Nov 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SP

Sreekanth Puli

SD

Sujeeth Desu

STB

Sai Tarun Boddu

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Abstract

In the HR recruitment process, one pivotal stage is “Summarizing & Screening.” At this juncture, recruiters grapple with the laborious task of manually sifting through a myriad of resumes to shortlist suitable candidates. This project aims to streamline and automate this process using two innovative tools. Firstly, the “Resume Summarizer” leverages advanced natural language processing techniques to swiftly extract pertinent details from candidates’ resumes, offering a concise summary that underscores their skills, experience, and qualifications. This summary can be tailored to match specific job requirements, facilitating recruiters in the comparative evaluation of multiple candidates. Secondly, the “Job Description Matcher” assists recruiters in expeditiously and accurately aligning job descriptions with candidate resumes. By employing natural language processing, this tool identifies

KEYWORDS

and phrases that align with the job opening’s prerequisites generating a compatibility score to rank resumes according to their alignment with the job description

Cite This Article

Sreekanth Puli, Sujeeth Desu, Sai Tarun Boddu, "RESUME SUMMARIZER AND JOB DESCRIPTION MATCHER USING NATURAL LANGUAGE PROCESSING AND SPACY," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 11, Nov 2023. https://doi.org/10.55041/IJSREM26647

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Publication Date

11/10/2023

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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