Domain Controlled Title Generation with Human Evaluation
This work addresses the problem of generating catchy and domain-specific titles for scientific articles, which is an incremental improvement in text generation.
The authors tackled automatic title generation for scientific articles by developing a domain-controlled method using a pre-trained transformer and additional tokens, resulting in titles that were rated higher in automated and human evaluations compared to original titles.
We study automatic title generation and present a method for generating domain-controlled titles for scientific articles. A good title allows you to get the attention that your research deserves. A title can be interpreted as a high-compression description of a document containing information on the implemented process. For domain-controlled titles, we used the pre-trained text-to-text transformer model and the additional token technique. Title tokens are sampled from a local distribution (which is a subset of global vocabulary) of the domain-specific vocabulary and not global vocabulary, thereby generating a catchy title and closely linking it to its corresponding abstract. Generated titles looked realistic, convincing, and very close to the ground truth. We have performed automated evaluation using ROUGE metric and human evaluation using five parameters to make a comparison between human and machine-generated titles. The titles produced were considered acceptable with higher metric ratings in contrast to the original titles. Thus we concluded that our research proposes a promising method for domain-controlled title generation.