论文标题
变形金刚从科学摘要端到端创造（幽默的）标题来获得LOL：
Transformers Go for the LOLs: Generating (Humourous) Titles from Scientific Abstracts End-to-End
论文作者
论文摘要
我们考虑了端到端的抽象到标题生成问题，探索了七个基于变压器的模型（包括chatgpt），对来自NLP和机器学习（ML）场所的30k抽象纹章对进行了微调。作为扩展，我们还考虑了产生幽默纸质标题的更困难的问题。对于后者，我们为NLP/ML域中的科学论文编辑了第一个大规模幽默注释数据集，其中包括〜2.6K标题。我们使用人类和自动指标评估所有模型。我们的人类评估表明，我们最佳的端到端系统的表现与人类作者类似（但可以说稍差）。但是，生成有趣的标题更加困难，我们的自动系统显然相对于人类而言显然不足，并且经常学习幽默的数据集文物。最后，Chatgpt没有任何微调，就可以在我们最好的微调系统的级别上进行。
We consider the end-to-end abstract-to-title generation problem, exploring seven recent transformer based models (including ChatGPT) fine-tuned on more than 30k abstract-title pairs from NLP and machine learning (ML) venues. As an extension, we also consider the harder problem of generating humorous paper titles. For the latter, we compile the first large-scale humor annotated dataset for scientific papers in the NLP/ML domains, comprising almost ~2.6k titles. We evaluate all models using human and automatic metrics. Our human evaluation suggests that our best end-to-end system performs similarly to human authors (but arguably slightly worse). Generating funny titles is more difficult, however, and our automatic systems clearly underperform relative to humans and often learn dataset artefacts of humor. Finally, ChatGPT, without any fine-tuning, performs on the level of our best fine-tuned system.
