  Transformer-based language models, including ChatGPT, have demonstrated
exceptional performance in various natural language generation tasks. However,
there has been limited research evaluating ChatGPT's keyphrase generation
ability, which involves identifying informative phrases that accurately reflect
a document's content. This study seeks to address this gap by comparing
ChatGPT's keyphrase generation performance with state-of-the-art models, while
also testing its potential as a solution for two significant challenges in the
field: domain adaptation and keyphrase generation from long documents. We
conducted experiments on six publicly available datasets from scientific
articles and news domains, analyzing performance on both short and long
documents. Our results show that ChatGPT outperforms current state-of-the-art
models in all tested datasets and environments, generating high-quality
keyphrases that adapt well to diverse domains and document lengths.
