  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.
