Global models of document structure using latent permutations

• Main Authors: Chen, Harr (Contributor), Branavan, Satchuthanan R. (Contributor), Barzilay, Regina (Contributor), Karger, David R. (Contributor)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Association for Computational Linguistics, 2010-10-14T12:43:57Z.
• Subjects: Article
• Online Access: Get fulltext

 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 elegantly represented using a distribution over permutations called the generalized Mallows model. Our structure-aware approach substantially outperforms alternative approaches for cross-document comparison and single-document segmentation.