Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in NLP, pages 32--39,

In this paper we examine topic segmentation of narrative documents, which are characterized by long passages of text with few headings. We first present results suggesting that previous topic segmentation approaches are not appropriate for narrative text. We then present a feature-based method that combines features from diverse sources as well as learned features. Applied to narrative books and encyclopedia articles, our method shows results that are significantly better than previous segmentation approaches. An analysis of individual features is also provided and the benefit of generalization using outside resources is shown.

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