Paper: Predicting Interesting Things in Text

ACL ID C14-1140
Title Predicting Interesting Things in Text
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

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 interesting- ness 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 tr...