%0 Conference Proceedings %T Global Voices: Crossing Borders in Automatic News Summarization %A Nguyen, Khanh %A Daumé III, Hal %Y Wang, Lu %Y Cheung, Jackie Chi Kit %Y Carenini, Giuseppe %Y Liu, Fei %S Proceedings of the 2nd Workshop on New Frontiers in Summarization %D 2019 %8 November %I Association for Computational Linguistics %C Hong Kong, China %F nguyen-daume-iii-2019-global %X 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. %R 10.18653/v1/D19-5411 %U https://aclanthology.org/D19-5411 %U https://doi.org/10.18653/v1/D19-5411 %P 90-97