参考：
canonical
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:id_liputan6/canonical')
- 说明：
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.
- 许可：无已知许可
- 版本：1.0.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'test' |
10972 |
'train' |
193883 |
'validation' |
10972 |
- 特征：
{
"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"
}
}
xtreme
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:id_liputan6/xtreme')
- 说明：
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.
- 许可：无已知许可
- 版本：1.0.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'test' |
3862 |
'validation' |
4948 |
- 特征：
{
"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"
}
}