Datasets:

Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
ArXiv:
License:

The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider removing the loading script and relying on automated data support (you can use convert_to_parquet from the datasets library). If this is not possible, please open a discussion for direct help.

Dataset Card for "scientific_papers"

Dataset Summary

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, paragraphs separated by "/n".
  • abstract: the abstract of the document, paragraphs separated by "/n".
  • section_names: titles of sections, separated by "/n".

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

arxiv

  • Size of downloaded dataset files: 4.50 GB
  • Size of the generated dataset: 7.58 GB
  • Total amount of disk used: 12.09 GB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "abstract": "\" we have studied the leptonic decay @xmath0 , via the decay channel @xmath1 , using a sample of tagged @xmath2 decays collected...",
    "article": "\"the leptonic decays of a charged pseudoscalar meson @xmath7 are processes of the type @xmath8 , where @xmath9 , @xmath10 , or @...",
    "section_names": "[sec:introduction]introduction\n[sec:detector]data and the cleo- detector\n[sec:analysys]analysis method\n[sec:conclusion]summary"
}

pubmed

  • Size of downloaded dataset files: 4.50 GB
  • Size of the generated dataset: 2.51 GB
  • Total amount of disk used: 7.01 GB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "abstract": "\" background and aim : there is lack of substantial indian data on venous thromboembolism ( vte ) . \\n the aim of this study was...",
    "article": "\"approximately , one - third of patients with symptomatic vte manifests pe , whereas two - thirds manifest dvt alone .\\nboth dvt...",
    "section_names": "\"Introduction\\nSubjects and Methods\\nResults\\nDemographics and characteristics of venous thromboembolism patients\\nRisk factors ..."
}

Data Fields

The data fields are the same among all splits.

arxiv

  • article: a string feature.
  • abstract: a string feature.
  • section_names: a string feature.

pubmed

  • article: a string feature.
  • abstract: a string feature.
  • section_names: a string feature.

Data Splits

name train validation test
arxiv 203037 6436 6440
pubmed 119924 6633 6658

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information

@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}
}

Contributions

Thanks to @thomwolf, @jplu, @lewtun, @patrickvonplaten for adding this dataset.

Downloads last month
23
Edit dataset card

Models trained or fine-tuned on armanc/scientific_papers

Space using armanc/scientific_papers 1