  We construct Global Voices, a multilingual dataset for evaluating
cross-lingual summarization methods. We extract social-network descriptions of
Global Voices news articles to cheaply collect evaluation data for into-English
and from-English summarization in 15 languages. Especially, for the
into-English summarization task, we crowd-source a high-quality evaluation
dataset based on guidelines that emphasize accuracy, coverage, and
understandability. To ensure the quality of this dataset, we collect human
ratings to filter out bad summaries, and conduct a survey on humans, which
shows that the remaining summaries are preferred over the social-network
summaries. We study the effect of translation quality in cross-lingual
summarization, comparing a translate-then-summarize approach with several
baselines. Our results highlight the limitations of the ROUGE metric that are
overlooked in monolingual summarization. Our dataset is available for download
at https://forms.gle/gpkJDT6RJWHM1Ztz9 .
