The title of a research paper conveys its primary idea and, occasionally, its conclusions in a clear and concise manner. Choosing an appropriate title is often challenging, and automated title generation can assist authors in this task. In this work, we propose a technique to generate paper titles from abstracts using open-weight pre-trained and large language models. We use the CSPubSum and LREC-COLING-2024 datasets and introduce a new dataset, SpringerSSAT, curated from four Springer journals in the social sciences. Additionally, we use GPT-3.5-turbo in a zero-shot setting to generate titles. Model performance is evaluated with ROUGE, METEOR, MoverScore, BERTScore, and SciBERTScore metrics. Our experiments show that fine-tuned PEGASUS-large outperforms other models, including fine-tuned LLaMA-3-8B and zero-shot GPT-3.5-turbo, across most metrics. We further demonstrate that ChatGPT can generate creative paper titles. Overall, AI-generated titles are generally appropriate and reliable.

翻译：研究论文的标题以清晰简洁的方式传达其核心思想，有时也包含其结论。选择合适的标题往往颇具挑战性，而自动化标题生成可在此任务中为作者提供帮助。本工作中，我们提出了一种利用开放权重预训练大语言模型，从摘要生成论文标题的技术。我们使用了CSPubSum和LREC-COLING-2024数据集，并引入了一个新数据集SpringerSSAT，该数据集从社会科学领域的四本Springer期刊中整理而来。此外，我们在零样本场景下使用GPT-3.5-turbo来生成标题。模型性能通过ROUGE、METEOR、MoverScore、BERTScore和SciBERTScore指标进行评估。实验表明，经过微调的PEGASUS-large在大多数指标上优于其他模型，包括微调的LLaMA-3-8B和零样本GPT-3.5-turbo。我们进一步证明，ChatGPT能够生成富有创意的论文标题。总体而言，AI生成的标题通常是恰当且可靠的。