  Providing visual summaries of scientific publications can increase
information access for readers and thereby help deal with the exponential
growth in the number of scientific publications. Nonetheless, efforts in
providing visual publication summaries have been few and far apart, primarily
focusing on the biomedical domain. This is primarily because of the limited
availability of annotated gold standards, which hampers the application of
robust and high-performing supervised learning techniques. To address these
problems we create a new benchmark dataset for selecting figures to serve as
visual summaries of publications based on their abstracts, covering several
domains in computer science. Moreover, we develop a self-supervised learning
approach, based on heuristic matching of inline references to figures with
figure captions. Experiments in both biomedical and computer science domains
show that our model is able to outperform the state of the art despite being
self-supervised and therefore not relying on any annotated training data.
