id_liputan6

Références:

canonique

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:id_liputan6/canonical')
  • Descriptif :
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
  • Fractionnements :
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')
  • Descriptif :
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
  • Fractionnements :
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"
    }
}