scientific_papers

  • Description:

Scientific papers datasets contains two sets of long and structured documents. The datasets are obtained from ArXiv and PubMed OpenAccess repositories.

Both "arxiv" and "pubmed" have two features: - article: the body of the document, pagragraphs seperated by "/n". - abstract: the abstract of the document, pagragraphs seperated by "/n". - section_names: titles of sections, seperated by "/n".

FeaturesDict({
    'abstract': Text(shape=(), dtype=tf.string),
    'article': Text(shape=(), dtype=tf.string),
    'section_names': Text(shape=(), dtype=tf.string),
})
@article{Cohan_2018,
   title={A Discourse-Aware Attention Model for Abstractive Summarization of
            Long Documents},
   url={http://dx.doi.org/10.18653/v1/n18-2097},
   DOI={10.18653/v1/n18-2097},
   journal={Proceedings of the 2018 Conference of the North American Chapter of
          the Association for Computational Linguistics: Human Language
          Technologies, Volume 2 (Short Papers)},
   publisher={Association for Computational Linguistics},
   author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},
   year={2018}
}

scientific_papers/arxiv (default config)

  • Config description: Documents from ArXiv repository.
  • Splits:
Split Examples
'test' 6,440
'train' 203,037
'validation' 6,436

scientific_papers/pubmed

  • Config description: Documents from PubMed repository.
  • Splits:
Split Examples
'test' 6,658
'train' 119,924
'validation' 6,633