task_categories:summarization task_ids:news-articles-summarization annotations_creators:no-annotation language_creators:found multilinguality:monolingual size_categories:100K<n<1M source_datasets:original language:id license:unknown extractive-summarization arxiv:2011.00679 region:us
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.
@inproceedings{id_liputan6,
  author    = {Fajri Koto, Jey Han Lau, Timothy Baldwin},
  title     = {Liputan6: A Large-scale Indonesian Dataset for Text Summarization},
  year      = {2020},
  url       = {https://arxiv.org/abs/2011.00679},
}
---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: null
pretty_name: Large-scale Indonesian Summarization
tags:
- extractive-summarization
dataset_info:
- config_name: canonical
  features:
  - name: id
    dtype: string
  - name: url
    dtype: string
  - name: clean_article
    dtype: string
  - name: clean_summary
    dtype: string
  - name: extractive_summary
    dtype: string
  splits:
  - name: validation
    num_bytes: 20944658
    num_examples: 10972
  - name: test
    num_bytes: 20526768
    num_examples: 10972
  - name: train
    num_bytes: 382245586
    num_examples: 193883
  download_size: 0
  dataset_size: 423717012
- config_name: xtreme
  features:
  - name: id
    dtype: string
  - name: url
    dtype: string
  - name: clean_article
    dtype: string
  - name: clean_summary
    dtype: string
  - name: extractive_summary
    dtype: string
  splits:
  - name: validation
    num_bytes: 9652946
    num_examples: 4948
  - name: test
    num_bytes: 7574550
    num_examples: 3862
  download_size: 0
  dataset_size: 17227496
---

# Dataset Card for Large-scale Indonesian Summarization

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [IndoLEM (Indonesian Language Evaluation Montage)](https://indolem.github.io/)
- **Repository:** [Liputan6: Summarization Corpus for Indonesian](https://github.com/fajri91/sum_liputan6/)
- **Paper:** https://arxiv.org/abs/2011.00679
- **Leaderboard:**
- **Point of Contact:** [Fajri Koto](mailto:feryandi.n@gmail.com),
[Jey Han Lau](mailto:jeyhan.lau@gmail.com), [Timothy Baldwin](mailto:tbaldwin@unimelb.edu.au), 

### Dataset Summary

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.

The dataset has two variants: "canonical" and "xtreme". The "xtreme" variant discards development and test 
document–summary pairs where the summary has fewer than 90% novel 4-grams (the training data remains the same 
as the canonical variant).

You need to manually request the liputan6 dataset using the form in https://github.com/fajri91/sum_liputan6/
and uncompress it. The liputan6 dataset can then be loaded using the following command 
`datasets.load_dataset("id_liputan6", 'canonical', data_dir="<path/to/uncompressed_folder>")` or
`datasets.load_dataset("id_liputan6", 'xtreme', data_dir="<path/to/uncompressed_folder>")`.
### Supported Tasks and Leaderboards

[More Information Needed]

### Languages
Indonesian

## Dataset Structure
```
{
  'id': 'string',
  'url': 'string',
  'clean_article': 'string',
  'clean_article': 'string',
  'extractive_summary': 'string'
}
```
### Data Instances

An example of the dataset:
```
{
  'clean_article': 'Liputan6.com, Ambon: Partai Bulan Bintang wilayah Maluku bertekad membantu pemerintah menyelesaikan konflik di provinsi tersebut. Syaratnya, penanganan penyelesaian konflik Maluku harus dimulai dari awal kerusuhan, yakni 19 Januari 1999. Demikian hasil Musyawarah Wilayah I PBB Maluku yang dimulai Sabtu pekan silam dan berakhir Senin (31/12) di Ambon. Menurut seorang fungsionaris PBB Ridwan Hasan, persoalan di Maluku bisa selesai asalkan pemerintah dan aparat keamanan serius menangani setiap persoalan di Maluku secara komprehensif dan bijaksana. Itulah sebabnya, PBB wilayah Maluku akan menjadikan penyelesaian konflik sebagai agenda utama partai. PBB Maluku juga akan mendukung penegakan hukum secara terpadu dan tanpa pandang bulu. Siapa saja yang melanggar hukum harus ditindak. Ridwan berharap, Ketua PBB Maluku yang baru, Ali Fauzi, dapat menindak lanjuti agenda politik partai yang telah diamanatkan dan mau mendukung penegakan hukum di Maluku. (ULF/Sahlan Heluth).',
  'clean_summary': 'Konflik Ambon telah berlangsung selama tiga tahun. Partai Bulan Bintang wilayah Maluku siap membantu pemerintah menyelesaikan kasus di provinsi tersebut.',
  'extractive_summary': 'Liputan6.com, Ambon: Partai Bulan Bintang wilayah Maluku bertekad membantu pemerintah menyelesaikan konflik di provinsi tersebut. Siapa saja yang melanggar hukum harus ditindak.',
  'id': '26408',
  'url': 'https://www.liputan6.com/news/read/26408/pbb-siap-membantu-penyelesaian-konflik-ambon'
}

```

### Data Fields
- `id`: id of the sample
- `url`: the url to the original article
- `clean_article`: the original article
- `clean_article`: the abstractive summarization
- `extractive_summary`: the extractive summarization

### Data Splits

The dataset is splitted in to train, validation and test sets.

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?
[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
```
@inproceedings{Koto2020Liputan6AL,
  title={Liputan6: A Large-scale Indonesian Dataset for Text Summarization},
  author={Fajri Koto and Jey Han Lau and Timothy Baldwin},
  booktitle={AACL/IJCNLP},
  year={2020}
}
```
### Contributions

Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.