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.

翻译：基于Transformer的语言模型，包括ChatGPT，在各种自然语言生成任务中展现了卓越的性能。然而，针对ChatGPT在关键词生成能力方面的评估研究仍较为有限，该能力涉及识别能准确反映文档内容的富含信息的关键词短语。本研究旨在通过将ChatGPT的关键词生成性能与最先进模型进行对比，填补这一研究空白，同时检验其作为解决该领域两个重大挑战（领域自适应与长文档关键词生成）的潜在方案。我们在来自科学文章和新闻领域的六个公开数据集上进行了实验，分析了短文档和长文档上的性能表现。结果表明，在所有测试数据集和环境下，ChatGPT均优于当前最先进模型，生成了能够良好适应不同领域和文档长度的高质量关键词。