During reading, students construct mental models of what they read. Summaries can be used to evaluate the latent knowledge structure of these mental models. We used indices from Student Mental Model Analyzer for Research and Teaching (SMART) to explore the potential of a global index, Graph Centrality (GC), as a measure to describe mental model structure and its relation to the quality of student summaries (e.g., the amount of content-coverage). Students (n = 73) in an online graduate-level course wrote and revised summaries of their course readings. Data preview left the total count of 32 cases to evaluate how students' mental representations changed from initial to final version. These summaries were analyzed using indices derived from the 3S model (surface, structure, semantic) as well as a measure of GC. The results of this initial investigation are promising, demonstrating that Graph Centrality captures important differences in students' summaries, including revision behaviors to the wholistic structure of mental models, modification trajectories toward a cohesive and solid mental representation that is semantically similar to the expert model.