DTATG: An Automatic Title Generator based on Dependency Trees
This addresses the problem of generating concise titles for text blocks, but it is incremental as it builds on existing methods with specific improvements.
The authors tackled automatic title generation from text by proposing DTATG, which extracts central sentences, compresses them via dependency trees, and selects the best candidate, resulting in higher F1 scores compared to previous methods.
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.