DTATG: An Automatic Title Generator based on Dependency Trees

Liqun Shao, Jie Wang

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.

References

  1. David M. Blei, A. Y. N. and Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of machine Learning research, 3(2003): 993-1022.
  2. D. Greene and P. Cunningham. (2006). Practical solutions to the problem of diagonal dominance in kernel document clustering. In ICML.
  3. Kevin Knight and Daniel Marcu. (2002). Summarization beyond sentence extraction: A probabilistic approach to sentence compression. In Artificial Intelligence , 139(1): 91-107.
  4. Rong Jin and Alexander G. Hauptmann. (2001). Automatic Title Generation for Spoken Broadcast News. In Proceedings of HLT-01, 2001, pp. 1lC3.
  5. Stuart Rose, Dave Engel, Nick Cramer and Wendy Cowley. (2010). Automatic keyword extraction from individual documents. In Text mining applications and theory, 2010, pp. 3-19.
  6. Jenine Turner and Eugene Charniak. (2005). Supervised and unsupervised learning for sentence compression. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, Ann Arbor, Mich., 25-30 June 2005, pp. 290-297.
  7. Vincent Vandegehinste and Yi Pan. (2004). Sentence compression for automated subtitling: A hybrid approach. In Proceedings of the ACL-04, 2004 (pp. 89-95).
  8. Y. Matsuo and M. Ishizuka. (2004). Keyword extraction from a single document using word co-occurrence statistical information. International Journal on Artificial Intelligence Tools, 13(1): 157-169.
  9. Sylvain Kahane. (2012). Why to choose dependency rather than constituency for syntax: a formal point of view. In Meanings, Texts, and other exciting things: A Festschrift to Commemorate the 80th Anniversary of Professor Igor A. Mel'c?uk, Languages of Slavic Culture, Moscou, pp. 257-272.
  10. Yonglei Zhang, Cheng Peng, and Hongling Wang. (2013). Research on Chinese Sentence Compression for the Title Generation. Chinese Lexical Semantics, edited by Xiao Guozheng & Ji Donhong. Heideberg: Springer.
Download


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