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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 QuestEval: Summarization Asks for Fact-based Evaluation Scialom, Thomas; Dray, Paul-Alexis; Gallinari, Patrick... In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing ; https://hal.sorbonne-universite.fr/hal-03541895 ; Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Nov 2021, Punta Cana (en ligne), Dominican Republic. pp.6594-6604, ⟨10.18653/v1/2021.emnlp-main.529⟩ ; https://2021.emnlp.org/ (2021) BASE Show details 2 MLSUM: The Multilingual Summarization Corpus Scialom, Thomas; Dray, Paul-Alexis; Lamprier, Sylvain... In: https://hal.sorbonne-universite.fr/hal-02989017 ; 2020 (2020) BASE Show details 3 MLSUM: The Multilingual Summarization Corpus Scialom, Thomas; Dray, Paul-Alexis; Lamprier, Sylvain... In: 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) ; https://hal.sorbonne-universite.fr/hal-03364407 ; 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2020, Online, France. pp.8051-8067, ⟨10.18653/v1/2020.emnlp-main.647⟩ (2020) BASE Show details 4 MLSUM: The Multilingual Summarization Corpus ... Scialom, Thomas; Dray, Paul-Alexis; Lamprier, Sylvain; Piwowarski, Benjamin; Staiano, Jacopo. - : arXiv, 2020 Abstract: We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2004.14900 https://dx.doi.org/10.48550/arxiv.2004.14900 BASE Hide details

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