Aphasia and dysarthria are both common symptoms of stroke, affecting around 30% and 50% of acute ischemic stroke patients. In this paper, we propose a storyline-centric approach to detect aphasia and dysarthria in acute stroke patients using transcribed picture descriptions alone. Our pipeline enriches the training set with healthy data to address the lack of acute stroke patient data and utilizes knowledge distillation to significantly improve upon a document classification baseline, achieving an AUC of 0.814 (aphasia) and 0.764 (dysarthria) on a patient-only validation set.