Large Language Models (LLMs) have gathered significant attention due to their impressive performance on a variety of tasks. ChatGPT, developed by OpenAI, is a recent addition to the family of language models and is being called a disruptive technology by a few, owing to its human-like text-generation capabilities. Although, many anecdotal examples across the internet have evaluated ChatGPT's strength and weakness, only a few systematic research studies exist. To contribute to the body of literature of systematic research on ChatGPT, we evaluate the performance of ChatGPT on Abstractive Summarization by the means of automated metrics and blinded human reviewers. We also build automatic text classifiers to detect ChatGPT generated summaries. We found that while text classification algorithms can distinguish between real and generated summaries, humans are unable to distinguish between real summaries and those produced by ChatGPT.

本研究通过使用自动度量和盲审人员评估 ChatGPT 在编写摘要上的表现，同时构建自动文本分类器以检测 ChatGPT 生成的摘要，并发现虽然文本分类算法可以区分真实和生成的摘要，但人类无法区分真实摘要和 ChatGPT 生成的摘要。

通过盲评审和文本分类算法比较ChatGPT生成的抽象概括和真实概括