Abstractive Text Summarization for Resumes With Cutting Edge NLP
  Transformers and LSTM
Öykü Berfin Mercan Sena Nur Cavsak Aysu Deliahmetoglu Senem Tanberk
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.