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An ongoing challenge in the analysis of document collections is how tosummarize content in terms of a set of inferred themes that can be interpretedsubstantively in terms of topics. The current practice of parametrizing thethemes in terms of most frequent words limits interpretability by ignoring thedifferential use of words across topics. We argue that words that are bothcommon and exclusive to a theme are more effective at characterizing topicalcontent. We consider a setting where professional editors have annotateddocuments to a collection of topic categories, organized into a tree, in whichleaf-nodes correspond to the most specific topics. Each document is annotatedto multiple categories, at different levels of the tree. We introduce ahierarchical Poisson convolution model to analyze annotated documents in thissetting. The model leverages the structure among categories defined byprofessional editors to infer a clear semantic description for each topic interms of words that are both frequent and exclusive. We carry out a largerandomized experiment on Amazon Turk to demonstrate that topic summaries basedon the FREX score are more interpretable than currently established frequencybased summaries, and that the proposed model produces more efficient estimatesof exclusivity than with currently models. We also develop a parallelizedHamiltonian Monte Carlo sampler that allows the inference to scale to millionsof documents.

A Poisson convolution model for characterizing topical content with word frequency and exclusivity