  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.
