Evaluating Text Summaries Generated by Large Language Models Using OpenAI's GPT
This work addresses the problem of evaluating text summaries for NLP researchers, offering a complementary tool to existing metrics, but it is incremental as it applies an existing method (GPT) to a new task (evaluation).
The study investigated using OpenAI's GPT models as independent evaluators for text summaries generated by six transformer-based models, finding significant correlations between GPT assessments and traditional metrics like ROUGE and LSA, particularly for relevance and coherence.
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 essential properties of high-quality summary - conciseness, relevance, coherence, and readability - using traditional metrics such as ROUGE and Latent Semantic Analysis (LSA). Uniquely, we also employed 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 GPT's potential as a robust tool for evaluating text summaries, offering insights that complement established metrics and providing a basis for comparative analysis of transformer-based models in natural language processing tasks.