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

TL;DR

This paper evaluates the performance of LSTM and pre-trained transformer models like T5, Pegasus, BART, and BART-Large on resume summarization and classification tasks, introducing a new resume dataset for benchmarking.

Contribution

It compares multiple NLP models on resume data and demonstrates that fine-tuned BART-Large achieves the best results for resume classification.

Findings

BART-Large fine-tuned on resumes outperforms other models.

Pre-trained transformers are effective for resume summarization.

The new resume dataset enables benchmarking in this domain.

Abstract

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.