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

A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems