Big data presents new challenges for understanding large text corpora. Topic modeling algorithms help understand the underlying patterns, or "topics", in data. Researchersauthor often read these topics in order to gain an understanding of the underlying corpus. It is important to evaluate the interpretability of these automatically generated topics. Methods have previously been designed to use crowdsourcing platforms to measure interpretability. In this paper, we demonstrate the necessity of a key concept, coherence, when assessing the topics and propose an effective method for its measurement. We show that the proposed measure of coherence captures a different aspect of the topics than existing measures. We further study the automation of these topic measures for scalability and reproducibility, showing that these measures can be automated.