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.

使用互动提示进行零翻译交叉语言生成任务，ChatGPT 可以在信息性和简洁性之间保持平衡，并显着提高其交叉语言生成任务性能。ChatGPT 在三个常见交叉语言生成数据集上的实验结果表明，其优于高级的 GPT 3.5 模型 (text-davinci-003)。