Measurements of the impact and history of research liter- ature provide a useful complement to scientific digital li- brary collections. Bibliometric indicators have been exten- sively studied, mostly in the context of journals. However, journal-based metrics poorly capture topical distinctions in fast-moving fields, and are increasingly problematic with the rise of open-access publishing. Recent developments in la- tent topic models have produced promising results for au- tomatic sub-field discovery. The fine-grained, faceted top- ics produced by such models provide a clearer view of the topical divisions of a body of research literature and the interactions between those divisions. We demonstrate the usefulness of topic models in measuring impact by applying a new phrase-based topic discovery model to a collection of 300,000 Computer Science publications, collected by the Rexa automatic citation indexing system.
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