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arXiv · 1401.3488

Content Modeling Using Latent Permutations

Harr Chen·S. R. K. Branavan·Regina Barzilay·David R. Karger
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Abstract

We present a novel Bayesian topic model for learning discourse-level document structure. Our model leverages insights from discourse theory to constrain latent topic assignments in a way that reflects the underlying organization of document topics. We propose a global model in which both topic selection and ordering are biased to be similar across a collection of related documents. We show that this space of orderings can be effectively represented using a distribution over permutations called the Generalized Mallows Model. We apply our method to three complementary discourse-level tasks: cross-document alignment, document segmentation, and information ordering. Our experiments show that incorporating our permutation-based model in these applications yields substantial improvements in performance over previously proposed methods.

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Harr Chen, S. R. K. Branavan, Regina Barzilay, David R. Karger. 2014-01-15. Content Modeling Using Latent Permutations. https://doi.org/10.1613/jair.2830

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