Abstract
Multi-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and empirical results-using several state-of-the-art models trained on the Multi-XScience dataset-reveal that Multi-XScience is well suited for abstractive models.
Cite
CITATION STYLE
Lu, Y., Dong, Y., & Charlin, L. (2020). Multi-XScience: A large-scale dataset for extreme multi-document summarization of scientific articles. In EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 8068–8074). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.emnlp-main.648
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