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Text documents of varying nature (e.g., summary documents written by analysts or published, scientific papers) often cite others as a means of providing evidence to support a claim, attributing credit, or referring the reader to related work. We address the problem of predicting a document's cited sources by introducing a novel, discriminative approach which combines a content-based generative model (LDA) with author-based features. Further, our classifier is able to learn the importance and quality of each topic within our corpus -- which can be useful beyond this task -- and preliminary results suggest its metric is competitive with other standard metrics (Topic Coherence). Our flagship system, Logit-Expanded, provides state-of-the-art performance on the largest corpus ever used for this task.
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