Human-like Summarization Evaluation with ChatGPT
This addresses the challenge of summarization evaluation for NLP researchers, but it is incremental as it applies an existing method (ChatGPT) to a new task.
The study tackled the problem of evaluating text summarization by exploring ChatGPT's ability to perform human-like evaluation using four methods across five datasets, finding that it outperformed common automatic metrics on some datasets.
Evaluating text summarization is a challenging problem, and existing evaluation metrics are far from satisfactory. In this study, we explored ChatGPT's ability to perform human-like summarization evaluation using four human evaluation methods on five datasets. We found that ChatGPT was able to complete annotations relatively smoothly using Likert scale scoring, pairwise comparison, Pyramid, and binary factuality evaluation. Additionally, it outperformed commonly used automatic evaluation metrics on some datasets. Furthermore, we discussed the impact of different prompts, compared its performance with that of human evaluation, and analyzed the generated explanations and invalid responses.