Resume Summarization—An Application of Generative AI

This project utilizes the fundamentals of Generative AI and NLP for abstractive text summarization and selection of the candidate resume, ease job application process by reducing human efforts and minimizing time consumption in candidate selection. This research explores summarization techniques using generative AI and natural language processing. The primary objective is to eliminate unnecessary content of resume while maintaining the readability of the document. Highlights the profile, skills, and work experience of the candidate. The project analyses a large number the given resume from candidates and elaborates the candidate’s main skills and experiences. It compares resumes and helps employers to find the best fit candidate for the specific job. Now a days there is huge competition in industries for a particular job role in various fields in industry. The ratio on single job position and number of candidates is extremely high. It is impossible to see every resume and figure out the best candidate out of it. Reading every section and points in resume of the thousands of resumes and finding the best resume for the job role seems impossible these days. There is a requirement of a system that can resolve this issue. This project converts the resume into a concise summary and elaborates the resume content in simple and enhances the readability of the text. Additionally, the explores the summarization abstractive and extractive summarization techniques and contrasts the optimal techniques for summarization. The project implements the combination of both summarization techniques using fine-tuned pre-trained T5 transformer, textRank model. Setups Generative adversarial networks (GANs) architecture and develops a fully automated resume summarization system.