We present the Embra system, a first-time entry to DUC for 2005 which performed at or above median for the manual assessment of responsiveness and on 4 out of 5 linguistic quality questions. The system takes a novel approach to relevance and redundancy, modeling sentence similarity using a latent semantic space constructed over a very large corpus. We present a simple approach to modeling specificity based on named entities which shows a small improvement over baseline. Finally, we discuss coherence and present a sentence reordering algorithm with a component-level evaluation demonstrating a positive effect.