

# Dataset Card for Multi-XScience

## Dataset Description

- **Homepage:** https://github.com/yaolu/Multi-XScience
- **Pubmed:** False
- **Public:** True
- **Tasks:** PARA,SUM


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}
}

```

multilinguality:monolingual language:en license:mit arxiv:2010.14235 region:us
{"language": ["en"], "license": "mit", "multilinguality": "monolingual", "pretty_name": "Multi-XScience", "bigbio_language": ["English"], "bigbio_license_shortname": "MIT", "homepage": "https://github.com/yaolu/Multi-XScience", "bigbio_pubmed": false, "bigbio_public": true, "bigbio_tasks": ["PARAPHRASING", "SUMMARIZATION"]}
TAGS
#multilinguality-monolingual #language-English #license-mit #arxiv-2010.14235 #region-us 



# Dataset Card for Multi-XScience

## Dataset Description

- Homepage: URL
- Pubmed: False
- Public: True
- Tasks: PARA,SUM


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.







# Dataset Card for Multi-XScience ## Dataset Description

- Homepage: URL
- Pubmed: False
- Public: True
- Tasks: PARA,SUM


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.
TAGS
#multilinguality-monolingual #language-English #license-mit #arxiv-2010.14235 #region-us 
 # Dataset Card for Multi-XScience ## Dataset Description

- Homepage: URL
- Pubmed: False
- Public: True
- Tasks: PARA,SUM


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.