Recent evaluation techniques applied to corpus-based systems have been introduced that can predict quantitatively how well surface realizers will generate unseen sentences in isolation. We introduce a similar method for determining the coverage on the Fuf/Surge symbolic surface realizer, report that its coverage and accuracy on the Penn TreeBank is higher than that of a similar statistics-based generator, describe several benefits that can be used in other areas of computational linguistics, and present an updated version of Surge for use in the NLG community.