Authors:
Shahrukh Bilal
Addresses:
Department of Computer Science and Technology, University of the West of Scotland, South Lanarkshire, Paisley, Scotland, United Kingdom.
Abstract:
This paper describes the creation of a smart web-based app that uses Natural Language Processing (NLP) and Machine Learning (ML) to automate the process of customising CVs. Users can create and manage a central profile in the system that includes their qualifications, work history, skills, and accomplishments. When it receives a job description, the application uses text analysis to identify key requirements and keywords. Then, these parts are compared with the user's profile to create a CV tailored to the job and meeting the employer's needs. The system improves job applications by ensuring they work with Applicant Tracking Systems (ATS) through improved keyword density, document structure, and formatting standards. This method greatly increases the chances that CVs will pass automated screening processes. The suggested fix aims to make it easier and faster to edit CVs by hand while also improving the accuracy and usefulness of the content. The app also supports many output formats, such as Word and PDF, and lets users customise things in real time with little effort. Experimental testing shows that the system is effective at producing high-quality, personalised CVs. This makes it a useful and scalable tool for job seekers in today's competitive hiring climate.
Keywords: Machine Learning; Deep Learning; Transformer Models; Text Summarisation; Abstractive Summarisation; Customer Reviews; Bert and Pegasus; Sentiment Analysis; Data Set.
Received on: 10/08/2025, Revised on: 29/09/2025, Accepted on: 16/10/2025, Published on: 05/03/2026
DOI: 10.64091/ATICR.2026.000310
AVE Trends in Intelligent Computing Research, 2026 Vol. 1 No. 1 , Pages: 65-90