Community created content (e.g., product descriptions, reviews) typically discusses one entity at a time and it can be hard as well as time consuming for a user to compare two or more entities. In response, we define a novel task of automatically generating entity comparisons from text. Our output is a table that semantically clusters descriptive phrases about entities. Our clustering algorithm is a Gaussian extension of probabilistic latent semantic analysis (pLSA), in which each phrase is represented in word vector embedding space. In addition, our algorithm attempts to balance information about entities in each cluster to generate meaningful comparison tables, where possible. We test our system's effectiveness on two domains, travel articles and movie reviews, and find that entity-balanced clusters are strongly preferred by users.
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