The task of matching job descriptions and resumes using machine learning algorithms to output a Skill Match Index in percentage is a complex problem. It involves several challenging tasks, including data cleaning and preparation, feature extraction, algorithm selection and training, and metric evaluation. To accomplish this task, expertise in NLP, machine learning, and data science, as well as a deep understanding of the job market and hiring practices, is required. However, with the help of new technologies in NLP, we aim to improve the performance of the ATS system.

So, there is a journal and research paper publication company Elsevier. They receive a copy of journals from authors and these journals have to go through various stages of copy editing tasks before getting published. Now, all these tasks were done manually previously and we were to automate some of the steps which could reduce the processing time, error and manually.