Machine learning offers a range of tools for training systems from data, but these methods are only as good as the underly- ing representation. This paper proposes to acquire representations for machine learn- ing by reading text written to accommo- date human learning. We propose a novel form of semantic analysis called read- ing to learn, where the goal is to obtain a high-level semantic abstract of multi- ple documents in a representation that fa- cilitates learning. We obtain this abstract through a generative model that requires no labeled data, instead leveraging repe- tition across multiple documents. The se- mantic abstract is converted into a trans- formed feature space for learning, result- ing in improved generalization on a rela- tional learning task.