upload hub_repos/multi_xscience/README.md to hub from bigbio repo

README.md

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+
+---
+language: 
+- en
+license: mit
+license_bigbio_shortname: MIT
+pretty_name: Multi-XScience
+---
+
+
+# Dataset Card for Multi-XScience
+
+## Dataset Description
+
+- **Homepage:** https://github.com/yaolu/Multi-XScience
+- **Pubmed:** False
+- **Public:** True
+- **Tasks:** Paraphrasing, Summarization
+
+
+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 t
+hat Multi-XScience is well suited for abstractive models.
+
+
+
+## Citation Information
+
+```
+@misc{https://doi.org/10.48550/arxiv.2010.14235,
+  doi = {10.48550/ARXIV.2010.14235},
+  
+  url = {https://arxiv.org/abs/2010.14235},
+  
+  author = {Lu, Yao and Dong, Yue and Charlin, Laurent},
+  
+  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  
+  title = {Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
+  
+  publisher = {arXiv},
+  
+  year = {2020},
+  
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+
+```