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--- |
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language: |
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- en |
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bigbio_language: |
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- English |
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license: mit |
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multilinguality: monolingual |
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bigbio_license_shortname: MIT |
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pretty_name: Multi-XScience |
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homepage: https://github.com/yaolu/Multi-XScience |
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bigbio_pubmed: False |
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bigbio_public: True |
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bigbio_tasks: |
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- PARAPHRASING |
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- SUMMARIZATION |
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--- |
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# Dataset Card for Multi-XScience |
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## Dataset Description |
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- **Homepage:** https://github.com/yaolu/Multi-XScience |
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- **Pubmed:** False |
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- **Public:** True |
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- **Tasks:** Paraphrasing, Summarization |
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Multi-document summarization is a challenging task for which there exists little large-scale datasets. |
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We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. |
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Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section |
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of a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, |
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a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and |
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empirical results---using several state-of-the-art models trained on the Multi-XScience dataset---reveal t |
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hat Multi-XScience is well suited for abstractive models. |
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## Citation Information |
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``` |
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@misc{https://doi.org/10.48550/arxiv.2010.14235, |
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doi = {10.48550/ARXIV.2010.14235}, |
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url = {https://arxiv.org/abs/2010.14235}, |
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author = {Lu, Yao and Dong, Yue and Charlin, Laurent}, |
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keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles}, |
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publisher = {arXiv}, |
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year = {2020}, |
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copyright = {arXiv.org perpetual, non-exclusive license} |
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} |
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``` |
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