DTATG: An Automatic Title Generator based on Dependency Trees

TL;DR

This paper introduces DTATG, an automatic title generation method that extracts central sentences, constructs dependency trees, compresses them, and selects the best title candidate, outperforming previous methods.

Contribution

The paper presents a novel dependency tree-based approach for automatic title generation, including a new compression model and title validation method.

Findings

DTATG generates adequate titles effectively.

Titles from DTATG have higher F1 scores than previous methods.

The approach improves title relevance and conciseness.

Abstract

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.