The creation of a large summarization quality dataset is a considerable, expensive, time-consuming effort, requiring careful planning and setup. It includes producing human-written and machine-generated summaries and evaluation of the summaries by humans, preferably by linguistic experts, and by automatic evaluation tools. If such effort is made in one language, it would be beneficial to be able to use it in other languages. To investigate how much we can trust the translation of such dataset without repeating human annotations in another language, we translated an existing English summarization dataset, SummEval dataset, to four different languages and analyzed the scores from the automatic evaluation metrics in translated languages, as well as their correlation with human annotations in the source language. Our results reveal that although translation changes the absolute value of automatic scores, the scores keep the same rank order and approximately the same correlations with human annotations.