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Domain Controlled Title Generation with Human Evaluation

EasyChair Preprint no. 4825

9 pagesDate: December 29, 2020


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 and human evaluation to make a comparison between human and
machine-generated titles. The titles produced were considered acceptable in human evaluation, thus we
concluded that our research proposes a promising method for domain-controlled title generation.

Keyphrases: automatic title generation, domain-control, human evaluation, Summarization Technique, Text-to-Text Transformer

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Abdul Waheed and Muskan Goyal and Nimisha Mittal and Deepak Gupta},
  title = {Domain Controlled Title Generation with Human Evaluation},
  howpublished = {EasyChair Preprint no. 4825},

  year = {EasyChair, 2020}}
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