Evaluating Text Summaries Generated by Large Language Models Using OpenAI's GPT

This research examines the effectiveness of OpenAI's GPT models as independent evaluators of text summaries generated by six transformer-based models from Hugging Face: DistilBART, BERT, ProphetNet, T5, BART, and PEGASUS. We evaluated these summaries based on the essential properties of a high-quality summary: concision, relevance, coherence, and readability using traditional metrics such as ROUGE and Latent Semantic Analysis (LSA). Uniquely, we employ GPT not as a summarizer, but as an evaluator, allowing it to independently assess summary quality without predefined metrics. Our analysis revealed significant correlations between GPT evaluations and traditional metrics, particularly in assessing relevance and coherence. The results demonstrate the potential of GPT as a robust tool for evaluating text summaries, providing insights that complement established metrics while providing a basis for comparative analysis of transformer-based models in natural language processing tasks.