# Global Voices: Crossing Borders in Automatic News Summarization

arXiv: https://arxiv.org/abs/1910.00421 · HTML: https://mlsanity.com/paper/1910.00421/

## Key facts

- Cited by 20 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 20 of 20)

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## Abstract (arXiv)

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 .