  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.
