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.