HEADS: Headline Generation as Sequence Prediction Using an Abstract Feature-Rich Space
Venue
Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL (NAACL'15), pp. 133-142
Publication Year
2015
Authors
Carlos A. Colmenares, Marina Litvak, Amin Mantrach, Fabrizio Silvestri
BibTeX
Abstract
Automatic headline generation is a sub-task of document summarization with many
reported applications. In this study we present a sequence-prediction technique for
learning how editors title their news stories. The introduced technique models the
problem as a discrete optimization task in a feature-rich space. In this space the
global optimum can be found in polynomial time by means of dynamic programming. We
train and test our model on an extensive corpus of financial news, and compare it
against a number of baselines by using standard metrics from the document
summarization domain, as well as some new ones proposed in this work. We also
assess the readability and informativeness of the generated titles through human
evaluation. The obtained results are very appealing and substantiate the soundness
of the approach.