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Large Language Models (LLMs) have gathered significant attention due to theirimpressive performance on a variety of tasks. ChatGPT, developed by OpenAI, isa recent addition to the family of language models and is being called adisruptive technology by a few, owing to its human-like text-generationcapabilities. Although, many anecdotal examples across the internet haveevaluated ChatGPT's strength and weakness, only a few systematic researchstudies exist. To contribute to the body of literature of systematic researchon ChatGPT, we evaluate the performance of ChatGPT on Abstractive Summarizationby the means of automated metrics and blinded human reviewers. We also buildautomatic text classifiers to detect ChatGPT generated summaries. We found thatwhile text classification algorithms can distinguish between real and generatedsummaries, humans are unable to distinguish between real summaries and thoseproduced by ChatGPT.

Comparing Abstractive Summaries Generated by ChatGPT to Real Summaries Through Blinded Reviewers and Text Classification Algorithms