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Text summarization is a fundamental task in natural language processing thataims to condense large amounts of textual information into concise and coherentsummaries. With the exponential growth of content and the need to extract keyinformation efficiently, text summarization has gained significant attention inrecent years. In this study, LSTM and pre-trained T5, Pegasus, BART andBART-Large model performances were evaluated on the open source dataset (Xsum,CNN/Daily Mail, Amazon Fine Food Review and News Summary) and the preparedresume dataset. This resume dataset consists of many information such aslanguage, education, experience, personal information, skills, and this dataincludes 75 resumes. The primary objective of this research was to classifyresume text. Various techniques such as LSTM, pre-trained models, andfine-tuned models were assessed using a dataset of resumes. The BART-Largemodel fine-tuned with the resume dataset gave the best performance.

Abstractive Text Summarization for Resumes With Cutting Edge NLP Transformers and LSTM