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        <title>MCLS: A Large-Scale Multimodal Cross-Lingual Summarization Dataset</title>
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    <name type="personal">
        <namePart type="given">Shi</namePart>
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            <title>Proceedings of the 22nd Chinese National Conference on Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Maosong</namePart>
            <namePart type="family">Sun</namePart>
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        <name type="personal">
            <namePart type="given">Bing</namePart>
            <namePart type="family">Qin</namePart>
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        <name type="personal">
            <namePart type="given">Xipeng</namePart>
            <namePart type="family">Qiu</namePart>
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            <namePart type="given">Jing</namePart>
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            <publisher>Chinese Information Processing Society of China</publisher>
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    <abstract>“Multimodal summarization which aims to generate summaries with multimodal inputs, e.g., textand visual features, has attracted much attention in the research community. However, previousstudies only focus on monolingual multimodal summarization and neglect the non-native readerto understand the cross-lingual news in practical applications. It inspires us to present a newtask, named Multimodal Cross-Lingual Summarization for news (MCLS), which generates cross-lingual summaries from multi-source information. To this end, we present a large-scale multimodalcross-lingual summarization dataset, which consists of 1.1 million article-summary pairs with 3.4million images in 44 * 43 language pairs. To generate a summary in any language, we propose aunified framework that jointly trains the multimodal monolingual and cross-lingual summarizationtasks, where a bi-directional knowledge distillation approach is designed to transfer knowledgebetween both tasks. Extensive experiments on many-to-many settings show the effectiveness ofthe proposed model.”</abstract>
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        <url>https://aclanthology.org/2023.ccl-1.73</url>
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        <date>2023-08</date>
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%0 Conference Proceedings
%T MCLS: A Large-Scale Multimodal Cross-Lingual Summarization Dataset
%A Xiaorui, Shi
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G English
%F xiaorui-2023-mcls
%X “Multimodal summarization which aims to generate summaries with multimodal inputs, e.g., textand visual features, has attracted much attention in the research community. However, previousstudies only focus on monolingual multimodal summarization and neglect the non-native readerto understand the cross-lingual news in practical applications. It inspires us to present a newtask, named Multimodal Cross-Lingual Summarization for news (MCLS), which generates cross-lingual summaries from multi-source information. To this end, we present a large-scale multimodalcross-lingual summarization dataset, which consists of 1.1 million article-summary pairs with 3.4million images in 44 * 43 language pairs. To generate a summary in any language, we propose aunified framework that jointly trains the multimodal monolingual and cross-lingual summarizationtasks, where a bi-directional knowledge distillation approach is designed to transfer knowledgebetween both tasks. Extensive experiments on many-to-many settings show the effectiveness ofthe proposed model.”
%U https://aclanthology.org/2023.ccl-1.73
%P 862-874

Markdown (Informal)

[MCLS: A Large-Scale Multimodal Cross-Lingual Summarization Dataset](https://aclanthology.org/2023.ccl-1.73) (Xiaorui, CCL 2023)

• MCLS: A Large-Scale Multimodal Cross-Lingual Summarization Dataset (Xiaorui, CCL 2023)

ACL

• Shi Xiaorui. 2023. MCLS: A Large-Scale Multimodal Cross-Lingual Summarization Dataset. In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 862–874, Harbin, China. Chinese Information Processing Society of China.