README.md

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Dataset Card for "scientific_papers"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits Sample Size
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://github.com/armancohan/long-summarization
• Repository: More Information Needed
• Paper: More Information Needed
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 8591.93 MB
• Size of the generated dataset: 9622.19 MB
• Total amount of disk used: 18214.12 MB

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, pagragraphs seperated by "/n".
• abstract: the abstract of the document, pagragraphs seperated by "/n".
• section_names: titles of sections, seperated by "/n".

Supported Tasks

More Information Needed

Languages

More Information Needed

Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

arxiv

• Size of downloaded dataset files: 4295.97 MB
• Size of the generated dataset: 7231.70 MB
• Total amount of disk used: 11527.66 MB

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: 4295.97 MB
• Size of the generated dataset: 2390.49 MB
• Total amount of disk used: 6686.46 MB

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 Sample Size

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

Dataset Creation

Curation Rationale

More Information Needed

Source Data

More Information Needed

Annotations

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.