This study investigates prompt-based learning for the automatic generation of academic paper highlights to address the lack of labeled training data in existing supervised methods. The researchers designed task-specific prompt templates combined with paper abstracts as inputs for several language models, including locally deployed GPT-2 and T5, as well as ChatGPT accessed via API. Experiments conducted on three datasets demonstrated that ChatGPT with prompt templates achieved performance comparable to previous supervised methods without requiring task-specific training samples. When a small number of examples were added to the prompts, the model significantly outperformed state-of-the-art methods on two of the datasets. The analysis revealed that while ChatGPT possesses strong language modeling capabilities, its performance is highly sensitive to the specific information provided within the prompt. Case studies indicated that the generated highlights are generally coherent, informative, and closely resemble those written by authors. This approach does not rely on domain-specific training corpora, supporting downstream text mining and bibliometric research for papers lacking existing highlights.