Abstract
In this paper we compare two parse-and-trim style headline generation systems. The Topiary system uses a statistical learning approach to finding topic labels for headlines, while our approach, the LexTrim system, identifies key summary words by analysing the lexical cohesion structure of a text. The performance of these systems is evaluated using the ROUGE evaluation suite on the DUC 2004 news stories collection.
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References
Lin, C.-Y., Hovy, E.: Automatic Evaluation of Summaries using n-gram Co-occurrence Statistics. In: The Proceedings of HLT/NACCL (2003)
Zajic, D., Dorr, B., Schwartz, R.: BBN/UMD at DUC 2004: Topiary. In: The Proceedings of the Document Understanding Conference, DUC (2004)
Dorr, B., Zajic, D., Schwartz, R., Hedge Trimmer: A Parse-and-Trim Approach to Headline Generation. In: The Proceedings of the Document Understanding Conference, DUC (2003)
Stokes, N.: Applications of Lexical Cohesion Analysis in the Topic Detection and Tracking domain. Ph.D. thesis. Department of Computer Science, University College Dublin (2004)
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© 2005 Springer-Verlag Berlin Heidelberg
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Wang, R., Stokes, N., Doran, W., Newman, E., Dunnion, J., Carthy, J. (2005). LexTrim: A Lexical Cohesion Based Approach to Parse-and-Trim Style Headline Generation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2005. Lecture Notes in Computer Science, vol 3406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30586-6_71
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DOI: https://doi.org/10.1007/978-3-540-30586-6_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24523-0
Online ISBN: 978-3-540-30586-6
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