This paper presents novel prompting techniques to improve the performance of automatic summarization systems for scientific articles. Scientific article summarization is highly challenging due to the length and complexity of these documents. We conceive, implement, and evaluate prompting techniques that provide additional contextual information to guide summarization systems. Specifically, we feed summarizers with lists of key terms extracted from articles, such as author keywords or automatically generated keywords. Our techniques are tested with various summarization models and input texts. Results show performance gains, especially for smaller models summarizing sections separately. This evidences that prompting is a promising approach to overcoming the limitations of less powerful systems. Our findings introduce a new research direction of using prompts to aid smaller models.

翻译：本文提出了新颖的提示技术，以提升科研论文自动摘要系统的性能。由于科研论文篇幅长、内容复杂，其自动摘要任务极具挑战性。我们构思、实现并评估了多种提示技术，通过提供额外上下文信息来引导摘要生成系统。具体而言，我们将从论文中提取的关键词列表（如作者提供的关键词或自动生成的关键词）输入给摘要生成器。我们在多种摘要模型和输入文本上测试了这些技术。实验结果表明，该方法在性能上有所提升，尤其是在对论文各章节分别进行摘要的小规模模型上效果显著。这证明提示技术是克服较弱系统局限性的一种有前景的方法。我们的发现开辟了利用提示技术辅助小模型进行摘要生成的新研究方向。