Job Descriptions Keyword Extraction using Attention based Deep Learning Models with BERT

Abstract

In this paper, we focus on creating a keywords extractor especially for a given job description job-related text corpus for better search engine optimization using attention based deep learning techniques. Millions of jobs are posted but most of them end up not being located due to improper SEO and keyword management. We aim to make this as easy to use as possible and allow us to use this for a large number of job descriptions very easily. We also make use of these algorithms to screen or get insights from large number of resumes, summarize and create keywords for a general piece of text or scientific articles. We also investigate the modeling power of BERT (Bidirectional Encoder Representations from Transformers) for the task of keyword extraction from job descriptions. We further validate our results by providing a fully-functional API and testing out the model with real-time job descriptions.

A Cosine Similarity-Based Resume Screening System for Job Recruitment

“…Many researchers have focused on developing resume screening systems based on other machine learning based approach. In the paper "Job Descriptions Keyword Extraction using Attention based Deep Learning Models with BERT (2021)", Hussain et al [8] presented an approach to extract keywords from job descriptions using attention-based deep learning models with BERT (Bidirectional Encoder Representations from Transformers). The proposed approach first preprocesses the job descriptions, and then uses BERT to encode the remaining text and extract meaningful keywords.…”