DTATG: An Automatic Title Generator based on Dependency Trees

Liqun Shao, Jie Wang

2016

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.

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Paper Citation


in Harvard Style

Shao L. and Wang J. (2016). DTATG: An Automatic Title Generator based on Dependency Trees . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 166-173. DOI: 10.5220/0006035101660173


in Bibtex Style

@conference{kdir16,
author={Liqun Shao and Jie Wang},
title={DTATG: An Automatic Title Generator based on Dependency Trees},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={166-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006035101660173},
isbn={978-989-758-203-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - DTATG: An Automatic Title Generator based on Dependency Trees
SN - 978-989-758-203-5
AU - Shao L.
AU - Wang J.
PY - 2016
SP - 166
EP - 173
DO - 10.5220/0006035101660173