Computational synthesis planning approaches have achieved recent success in\norganic chemistry, where tabulated synthesis procedures are readily available\nfor supervised learning. The syntheses of inorganic materials, however, exist\nprimarily as natural language narratives contained within scientific journal\narticles. This synthesis information must first be extracted from the text in\norder to enable analogous synthesis planning methods for inorganic materials.\nIn this work, we present a system for automatically extracting structured\nrepresentations of synthesis procedures from the texts of materials science\njournal articles that describe explicit, experimental syntheses of inorganic\ncompounds. We define the structured representation as a set of linked events\nmade up of extracted scientific entities and evaluate two unsupervised\napproaches for extracting these structures on expert-annotated articles: a\nstrong heuristic baseline and a generative model of procedural text. We also\nevaluate a variety of supervised models for extracting scientific entities. Our\nresults provide insight into the nature of the data and directions for further\nwork in this exciting new area of research.\n