  The volume of academic paper submissions and publications is growing at an
ever increasing rate. While this flood of research promises progress in various
fields, the sheer volume of output inherently increases the amount of noise. We
present a system to automatically separate papers with a high from those with a
low likelihood of gaining citations as a means to quickly find high impact,
high quality research. Our system uses both a visual classifier, useful for
surmising a document's overall appearance, and a text classifier, for making
content-informed decisions. Current work in the field focuses on small datasets
composed of papers from individual conferences. Attempts to use similar
techniques on larger datasets generally only considers excerpts of the
documents such as the abstract, potentially throwing away valuable data. We
rectify these issues by providing a dataset composed of PDF documents and
citation counts spanning a decade of output within two separate academic
domains: computer science and medicine. This new dataset allows us to expand on
current work in the field by generalizing across time and academic domain.
Moreover, we explore inter-domain prediction models - evaluating a classifier's
performance on a domain it was not trained on - to shed further insight on this
important problem.
