  Most Natural Language Generation systems need to produce accurate texts. We
propose a methodology for high-quality human evaluation of the accuracy of
generated texts, which is intended to serve as a gold-standard for accuracy
evaluations of data-to-text systems. We use our methodology to evaluate the
accuracy of computer generated basketball summaries. We then show how our gold
standard evaluation can be used to validate automated metrics
