metadata

language:
  - en
bigbio_language:
  - English
license: mit
multilinguality: monolingual
bigbio_license_shortname: MIT
pretty_name: Multi-XScience
homepage: https://github.com/yaolu/Multi-XScience
bigbio_pubmed: false
bigbio_public: true
bigbio_tasks:
  - PARAPHRASING
  - SUMMARIZATION

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}
}