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，以提升科学文献自动摘要系统的性能。由于科学文献篇幅冗长且内容复杂，对其自动摘要极具挑战性。我们设计、实现并评估了多种通过提供额外上下文信息来引导摘要系统的提示技术。具体而言，我们向摘要系统输入从文献中提取的关键词列表，包括作者关键词或自动生成的关键词。这些技术在不同摘要模型和输入文本上进行了测试。结果表明，该方法能有效提升性能，尤其适用于对章节进行独立摘要的较小模型。这证明提示技术是突破性能较弱系统局限性的有效途径。我们的发现开辟了利用提示辅助小模型这一新的研究方向。