README.md

metadata

annotations_creators:
  - found
language:
  - pt
language_creators:
  - found
license: afl-3.0
multilinguality:
  - monolingual
pretty_name: KittIA
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-generation
  - question-answering
task_ids: []
paperswithcode_id: null
tags:
  - not-for-all-audiences
dataset_info:
  - config_name: arxiv
    features:
      - name: article
        dtype: string
      - name: abstract
        dtype: string
      - name: section_names
        dtype: string
    splits:
      - name: train
        num_bytes: 7148341992
        num_examples: 203037
      - name: validation
        num_bytes: 217125524
        num_examples: 6436
      - name: test
        num_bytes: 217514961
        num_examples: 6440
    download_size: 4504646347
    dataset_size: 7582982477
  - config_name: pubmed
    features:
      - name: article
        dtype: string
      - name: abstract
        dtype: string
      - name: section_names
        dtype: string
    splits:
      - name: train
        num_bytes: 2252027383
        num_examples: 119924
      - name: validation
        num_bytes: 127403398
        num_examples: 6633
      - name: test
        num_bytes: 127184448
        num_examples: 6658
    download_size: 4504646347
    dataset_size: 2506615229

Dataset Card for "scientific_papers"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• 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:
• Repository: https://github.com/armancohan/long-summarization
• Paper: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 9.01 GB
• Size of the generated dataset: 10.09 GB
• Total amount of disk used: 19.10 GB

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.