Abstract
In this paper we compare a number of Topiary-style headline generation systems. The Topiary system, developed at the University of Maryland with BBN, was the top performing headline generation system at DUC 2004. Topiary-style headlines consist of a number of general topic labels followed by a compressed version of the lead sentence of a news story. 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 cohesive structure of a text. The performance of these systems is evaluated using the ROUGE evaluation suite on the DUC 2004 news stories collection. The results of these experiments show that a baseline system that identifies topic descriptors for headlines using term frequency counts outperforms the LexTrim and Topiary systems. A manual evaluation of the headlines also confirms this result.
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Wang, R., Stokes, N., Doran, W.P., Newman, E., Carthy, J., Dunnion, J. (2005). Comparing Topiary-Style Approaches to Headline Generation. In: Losada, D.E., Fernández-Luna, J.M. (eds) Advances in Information Retrieval. ECIR 2005. Lecture Notes in Computer Science, vol 3408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31865-1_12
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DOI: https://doi.org/10.1007/978-3-540-31865-1_12
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