Given a document in a source language, cross-lingual summarization (CLS) aims to generate a summary in a different target language. Recently, the emergence of ChatGPT has attracted wide attention from the computational linguistics community. However, it is not yet known the performance of ChatGPT on CLS. In this report, we empirically use various prompts to guide ChatGPT to perform zero-shot CLS from different paradigms (i.e., end-to-end and pipeline), and provide a preliminary evaluation on its generated summaries.We find that ChatGPT originally prefers to produce lengthy summaries with more detailed information. But with the help of an interactive prompt, ChatGPT can balance between informativeness and conciseness, and significantly improve its CLS performance. Experimental results on three widely-used CLS datasets show that ChatGPT outperforms the advanced GPT 3.5 model (i.e., text-davinci-003). In addition, we provide qualitative case studies to show the superiority of ChatGPT on CLS.

翻译：给定源语言文档，跨语言摘要（CLS）旨在生成不同目标语言的摘要。近期，ChatGPT的出现引起了计算语言学界的广泛关注。然而，ChatGPT在CLS任务上的表现尚不明确。在本报告中，我们通过实验性方法采用多种提示词，引导ChatGPT从不同范式（即端到端和流水线）执行零样本CLS，并对其生成的摘要进行了初步评估。我们发现，ChatGPT原始倾向于生成包含更详细信息的冗长摘要。但在交互式提示词的辅助下，ChatGPT能够平衡信息丰富性与简洁性，并显著提升其CLS性能。在三个广泛使用的CLS数据集上的实验结果表明，ChatGPT优于先进的GPT 3.5模型（即text-davinci-003）。此外，我们通过定性案例分析展示了ChatGPT在CLS任务上的优越性。