MODEX: A Web-Based Resume Generation System Powered by Transformer Models and Adaptive Template Rendering
Keywords:
Express.js, Large Language models (LLMs), Natural language processing (NLP), Node.js, PostgreSQL, React.js, Resume generation, Text summarizationAbstract
Creating an effective résumé is more difficult than it seems because it involves several important aspects: the proper writing style; proper formatting; and adding all the correct sections based on the job you are applying to. Most job seekers do not have the skill set necessary to create an effective résumé, causing many job seekers to have disorganized and ineffective résumés. To solve this problem, they have developed a web-based application known as MODEX which is built on a Node.js and Express.js backend with a React.js frontend and helps to make and improve résumés. The two main functions of MODEX are to generate a résumé from scratch via an online form and to improve existing résumés through the use of an intelligent parser that reformats an existing résumé. MODEX uses Large Language Model (LLM)-based techniques via API integration to perform resume parsing, named entity extraction, and content summarization to accomplish this task. Additionally, a React.js-based component-driven templating system allows rendering the created résumé into multiple customizable HTML layouts. All user-generated data will be collected and stored in a database for future retrieval. This paper will present the overall design of the system, the design and implementation of the RESTful service, and results from a user study with 35 participants that show how users were able to create their résumés more easily and efficiently with MODEX than they were before MODEX.
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