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Dataset: gigaword 🏷
Update gigaword.py on GitHub

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from datasets import load_dataset dataset = load_dataset("gigaword")

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Models trained or fine-tuned on gigaword



Dataset Card for "gigaword"

Table of Contents

Dataset Description

Dataset Summary

Headline-generation on a corpus of article pairs from Gigaword consisting of around 4 million articles. Use the 'org_data' provided by https://github.com/microsoft/unilm/ which is identical to https://github.com/harvardnlp/sent-summary but with better format.

There are two features:

  • document: article.
  • summary: headline.

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

default

  • Size of downloaded dataset files: 551.61 MB
  • Size of the generated dataset: 918.35 MB
  • Total amount of disk used: 1469.96 MB

An example of 'train' looks as follows.

{
    "document": "train source",
    "summary": "train target"
}

Data Fields

The data fields are the same among all splits.

default

  • document: a string feature.
  • summary: a string feature.

Data Splits Sample Size

name train validation test
default 3803957 189651 1951

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{graff2003english,
  title={English gigaword},
  author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki},
  journal={Linguistic Data Consortium, Philadelphia},
  volume={4},
  number={1},
  pages={34},
  year={2003}
}

@article{Rush_2015,
   title={A Neural Attention Model for Abstractive Sentence Summarization},
   url={http://dx.doi.org/10.18653/v1/D15-1044},
   DOI={10.18653/v1/d15-1044},
   journal={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
   publisher={Association for Computational Linguistics},
   author={Rush, Alexander M. and Chopra, Sumit and Weston, Jason},
   year={2015}
}