Abstractive Text Summarization for Resumes With Cutting Edge NLP Transformers and LSTM
This work addresses resume summarization for HR or recruitment applications, but it is incremental as it applies existing methods to a new dataset.
The study tackled resume text classification by evaluating LSTM and pre-trained transformer models like T5, Pegasus, and BART on open-source and resume datasets, finding that the BART-Large model fine-tuned on the resume dataset achieved the best performance, though no specific metrics or numbers were provided.
Text summarization is a fundamental task in natural language processing that aims to condense large amounts of textual information into concise and coherent summaries. With the exponential growth of content and the need to extract key information efficiently, text summarization has gained significant attention in recent years. In this study, LSTM and pre-trained T5, Pegasus, BART and BART-Large model performances were evaluated on the open source dataset (Xsum, CNN/Daily Mail, Amazon Fine Food Review and News Summary) and the prepared resume dataset. This resume dataset consists of many information such as language, education, experience, personal information, skills, and this data includes 75 resumes. The primary objective of this research was to classify resume text. Various techniques such as LSTM, pre-trained models, and fine-tuned models were assessed using a dataset of resumes. The BART-Large model fine-tuned with the resume dataset gave the best performance.