Global Voices: Crossing Borders in Automatic News Summarization
Khanh Duy Tung Nguyen, Hal Daumé · 2019
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 highquality 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.