# Domain Controlled Title Generation with Human Evaluation

arXiv: https://arxiv.org/abs/2103.05069 · HTML: https://mlsanity.com/paper/2103.05069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Semi-supervised Sequence Learning](https://mlsanity.com/paper/1511.01432/) (2015)
- [On the Use of ArXiv as a Dataset](https://mlsanity.com/paper/1905.00075/) (2019)
- [Domain Control for Neural Machine Translation](https://mlsanity.com/paper/1612.06140/) (2016)
- [Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation](https://mlsanity.com/paper/1904.07142/) (2019)
- [DTATG: An Automatic Title Generator based on Dependency Trees](https://mlsanity.com/paper/1710.00286/) (2017)

## Built on by (most cited first, 1 of 1)

- [Societal Impacts Research Requires Benchmarks for Creative Composition Tasks](https://mlsanity.com/paper/2504.06549/) (2025)

## Abstract (arXiv)

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.