Unstructured PDF documents remain the main vehicle for dissemination of scientific findings. Those interested in gathering and assimilating data must therefore manually peruse published articles and extract from these the elements of interest.
Evidence-based medicine provides a compelling illustration of this: many person-hours are spent each year extracting summary information from articles that describe clinical trials. Machine learning provides a potential means of mitigating this burden by automating extraction. But, for automated approaches to be useful to end-users, we need tools that allow domain experts to interact with, and benefit from, model predictions. To this end, we present an web-based tool called Spá that accepts as input an article and provides as output an automatically visually annotated rendering of this article. More generally, Spá provides a framework for visualizing predictions, both at the document and sentence level, for full-text PDFs.