metadata
pretty_name: Extreme Summarization (XSum) in Dutch
language:
- nl
paperswithcode_id: xsum_dutch
task_categories:
- summarization
task_ids:
- news-articles-summarization
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
Dataset Card for "xsum_dutch" 🇳🇱🇧🇪 Dataset
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
The Xsum Dutch 🇳🇱🇧🇪 Dataset is an English-language dataset translated to Dutch.
This dataset currently (Aug '22) has a single config, which is
config default of xsum translated to Dutch
with yhavinga/t5-base-36L-ccmatrix-multi.
- Homepage: https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 245.38 MB
- Size of the generated dataset: 507.60 MB
- Total amount of disk used: 752.98 MB
Dataset Summary
Extreme Summarization (XSum) Dataset.
There are three features:
- document: Input news article.
- summary: One sentence summary of the article.
- id: BBC ID of the article.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 245.38 MB
- Size of the generated dataset: 507.60 MB
- Total amount of disk used: 752.98 MB
An example of 'validation' looks as follows.
{
"document": "some-body",
"id": "29750031",
"summary": "some-sentence"
}
Data Fields
The data fields are the same among all splits.
default
document: astringfeature.summary: astringfeature.id: astringfeature.
Data Splits
| name | train | validation | test |
|---|---|---|---|
| default | 204045 | 11332 | 11334 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
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
@article{Narayan2018DontGM,
title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
journal={ArXiv},
year={2018},
volume={abs/1808.08745}
}
Contributions
Thanks to @thomwolf, @lewtun, @mariamabarham, @jbragg, @lhoestq, @patrickvonplaten for adding the English version of this dataset. The dataset was translated on Cloud TPU compute generously provided by Google through the TPU Research Cloud.