Comparing topiary-style approaches to headline generation

Abstract

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.