Références :
canonique
Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :
ds = tfds.load('huggingface:id_liputan6/canonical')
- Description :
In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from this http URL,
an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop
benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual
BERT-based models. We include a thorough error analysis by examining machine-generated summaries that have
low ROUGE scores, and expose both issues with ROUGE it-self, as well as with extractive and abstractive
summarization models.
- Licence : Aucune licence connue
- Version : 1.0.0
- Divisions :
Diviser | Exemples |
---|---|
'test' | 10972 |
'train' | 193883 |
'validation' | 10972 |
- Caractéristiques :
{
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"url": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"clean_article": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"clean_summary": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"extractive_summary": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
extrême
Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :
ds = tfds.load('huggingface:id_liputan6/xtreme')
- Description :
In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from this http URL,
an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop
benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual
BERT-based models. We include a thorough error analysis by examining machine-generated summaries that have
low ROUGE scores, and expose both issues with ROUGE it-self, as well as with extractive and abstractive
summarization models.
- Licence : Aucune licence connue
- Version : 1.0.0
- Divisions :
Diviser | Exemples |
---|---|
'test' | 3862 |
'validation' | 4948 |
- Caractéristiques :
{
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"url": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"clean_article": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"clean_summary": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"extractive_summary": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}