Scholarly documents contain many data entities, such as titles, authors, affiliations, figures, and tables. These entities can be used to enhance digital library services through enhanced metadata and enable the development of new services and tools for interacting with and exploring scholarly data. However, in a world of scholarly big data, extracting these entities in a scalable, efficient and accurate manner can be challenging. In this tutorial, we introduce the broad field of information extraction for scholarly digital libraries. Drawing on our experience in running the Cite-SeerX digital library, which has performed information extraction on over 7 million academic documents, we argue for the need for automatic information extraction, describe different approaches for performing information extraction, present tools and datasets that are readily available, and describe best practices and areas of research interest.