Predicting Interesting Things in Text

While reading a document, a user may encounter concepts, entities, and topics that she is interested in exploring more. We propose models of “interestingness”, which aim to predict the level of interest a user has in the various text spans in a document. We obtain naturally occurring interest signals by observing user browsing behavior in clicks from one page to another. We cast the problem of predicting interestingness as a discriminative learning problem over this data. We leverage features from two principal sources: textual context features and topic features that assess the semantics of the document transition. We learn our topic features without supervision via probabilistic inference over a graphical model that captures the latent joint topic space of the documents in the transition. We train and test our models on millions of realworld transitions between Wikipedia documents as observed from web browser session logs. On the task of predicting which spans are of most interest to users, we show significant improvement over various baselines and highlight the value of our latent semantic model.