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        <title>Global Voices: Crossing Borders in Automatic News Summarization</title>
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        <namePart type="given">Khanh</namePart>
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            <title>Proceedings of the 2nd Workshop on New Frontiers in Summarization</title>
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            <namePart type="given">Lu</namePart>
            <namePart type="family">Wang</namePart>
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    <abstract>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.</abstract>
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%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

Markdown (Informal)

[Global Voices: Crossing Borders in Automatic News Summarization](https://aclanthology.org/D19-5411) (Nguyen & Daumé III, 2019)

• Global Voices: Crossing Borders in Automatic News Summarization (Nguyen & Daumé III, 2019)

ACL

• Khanh Nguyen and Hal Daumé III. 2019. Global Voices: Crossing Borders in Automatic News Summarization. In Proceedings of the 2nd Workshop on New Frontiers in Summarization, pages 90–97, Hong Kong, China. Association for Computational Linguistics.