用预训练模型根据摘要自动生成论文标题，效果优于人工起名。
Can pre-trained language models generate titles for research papers?
- 微调PEGASUS-large模型从摘要生成标题
- 该模型在多个指标上优于LLaMA-3-8B和GPT-3.5-turbo
- 适合需要快速生成标题的研究者使用
论文标题以简洁方式传达研究主题，甚至有时包括核心发现。拟定合适标题常是繁重任务，若能自动化则对作者大有裨益。本文微调预训练语言模型，从论文摘要生成标题；同时使用GPT-3.5-turbo在零样本设置下生成标题。模型性能通过ROUGE、METEOR、MoverScore、BERTScore和SciBERTScore等指标衡量。结果表明，微调后的PEGASUS-large在多数指标上优于其他模型，包括微调的LLaMA-3-8B和GPT-3.5-turbo。我们还展示ChatGPT可生成富有创意的标题。观察表明，AI生成的论文标题通常准确且恰当。
原文摘要 · Abstract (English)
The title of a research paper communicates in a succinct style the main theme and, sometimes, the findings of the paper. Coming up with the right title is often an arduous task, and therefore, it would be beneficial to authors if title generation can be automated. In this paper, we fine-tune pre-trained language models to generate titles of papers from their abstracts. Additionally, we use GPT-3.5-turbo in a zero-shot setting to generate paper titles. The performance of the models is measured with ROUGE, METEOR, MoverScore, BERTScore and SciBERTScore metrics. We find that fine-tuned PEGASUS-large outperforms the other models, including fine-tuned LLaMA-3-8B and GPT-3.5-turbo, across most metrics. We also demonstrate that ChatGPT can generate creative titles for papers. Our observations suggest that AI-generated paper titles are generally accurate and appropriate.
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