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".

• Additional Documentation: Explore on Papers With Code north_east

• Homepage: https://github.com/armancohan/long-summarization

• Source code: tfds.datasets.scientific_papers.Builder

• Versions:

  • 1.1.0: No release notes.
  • 1.1.1 (default): No release notes.

• Download size: 4.20 GiB

• Auto-cached (documentation): No

• Feature structure:

FeaturesDict({
    'abstract': Text(shape=(), dtype=string),
    'article': Text(shape=(), dtype=string),
    'section_names': Text(shape=(), dtype=string),
})

• Feature documentation:

• Supervised keys (See as_supervised doc): ('article', 'abstract')

• Figure (tfds.show_examples): Not supported.

• Citation:

@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.

• Dataset size: 7.07 GiB

• Splits:

• Examples (tfds.as_dataframe):

scientific_papers/pubmed

• Config description: Documents from PubMed repository.

• Dataset size: 2.34 GiB

• Splits:

• Examples (tfds.as_dataframe):