# DTATG: An Automatic Title Generator based on Dependency Trees

arXiv: https://arxiv.org/abs/1710.00286 · HTML: https://mlsanity.com/paper/1710.00286/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [Domain Controlled Title Generation with Human Evaluation](https://mlsanity.com/paper/2103.05069/) (2021)
- [Text2Time: Transformer-based Article Time Period Prediction](https://mlsanity.com/paper/2304.10859/) (2023)

## Abstract (arXiv)

We study automatic title generation for a given block of text and present a method called DTATG to generate titles. DTATG first extracts a small number of central sentences that convey the main meanings of the text and are in a suitable structure for conversion into a title. DTATG then constructs a dependency tree for each of these sentences and removes certain branches using a Dependency Tree Compression Model we devise. We also devise a title test to determine if a sentence can be used as a title. If a trimmed sentence passes the title test, then it becomes a title candidate. DTATG selects the title candidate with the highest ranking score as the final title. Our experiments showed that DTATG can generate adequate titles. We also showed that DTATG-generated titles have higher F1 scores than those generated by the previous methods.