---
pretty_name: SEA Abstractive Summarization
license:
- cc-by-nc-sa-4.0
task_categories:
- text-generation
language:
- id
- ta
- th
- vi
dataset_info:
  features:
  - name: id
    dtype: string
  - name: label
    dtype: string
  - name: prompts
    list:
    - name: text
      dtype: string
  - name: prompt_templates
    sequence: string
  - name: metadata
    struct:
    - name: language
      dtype: string
    - name: url
      dtype: string
    - name: title
      dtype: string
  splits:
  - name: id
    num_bytes: 322112
    num_examples: 100
    num_tokens_gpt_4o: 61628
    num_tokens_gemma_2: 55485
    num_tokens_llama_3: 77016
  - name: id_fewshot
    num_bytes: 5963
    num_examples: 5
    num_tokens_gpt_4o: 1124
    num_tokens_gemma_2: 1050
    num_tokens_llama_3: 1430
  - name: ta
    num_bytes: 1075514
    num_examples: 100
    num_tokens_gpt_4o: 114275
    num_tokens_gemma_2: 156476
    num_tokens_llama_3: 457559
  - name: ta_fewshot
    num_bytes: 10198
    num_examples: 5
    num_tokens_gpt_4o: 964
    num_tokens_gemma_2: 1339
    num_tokens_llama_3: 3905
  - name: th
    num_bytes: 1201794
    num_examples: 100
    num_tokens_gpt_4o: 155203
    num_tokens_gemma_2: 151988
    num_tokens_llama_3: 176985
  - name: th_fewshot
    num_bytes: 8735
    num_examples: 5
    num_tokens_gpt_4o: 925
    num_tokens_gemma_2: 869
    num_tokens_llama_3: 1062
  - name: vi
    num_bytes: 395697
    num_examples: 100
    num_tokens_gpt_4o: 86305
    num_tokens_gemma_2: 78285
    num_tokens_llama_3: 82269
  - name: vi_fewshot
    num_bytes: 9092
    num_examples: 5
    num_tokens_gpt_4o: 2396
    num_tokens_gemma_2: 2170
    num_tokens_llama_3: 2282
  download_size: 1258846
  dataset_size: 3029105
  total_tokens_gpt_4o: 422820
  total_tokens_gemma_2: 447662
  total_tokens_llama_3: 802508
configs:
- config_name: default
  data_files:
  - split: id
    path: data/id-*
  - split: id_fewshot
    path: data/id_fewshot-*
  - split: ta
    path: data/ta-*
  - split: ta_fewshot
    path: data/ta_fewshot-*
  - split: th
    path: data/th-*
  - split: th_fewshot
    path: data/th_fewshot-*
  - split: vi
    path: data/vi-*
  - split: vi_fewshot
    path: data/vi_fewshot-*
size_categories:
- n<1K
---

# SEA Abstractive Summarization

SEA Abstractive Summarization evaluates a model's ability to read a document, identify the key points within, and summarize them into a coherent and fluent text while paraphrasing the document. It is sampled from [XL-Sum](https://aclanthology.org/2021.findings-acl.413/) for Indonesian, Tamil, Thai, and Vietnamese.

### Supported Tasks and Leaderboards

SEA Abstractive Summarization is designed for evaluating chat or instruction-tuned large language models (LLMs). It is part of the [SEA-HELM](https://leaderboard.sea-lion.ai/) leaderboard from [AI Singapore](https://aisingapore.org/).

### Languages
- Indonesian (id)
- Tamil (ta)
- Thai (th)
- Vietnamese (vi)

### Dataset Details
SEA Abstractive Summarization is split by language, with additional splits containing fewshot examples. Below are the statistics for this dataset. The number of tokens only refer to the strings of text found within the `prompts` column.

| Split | # of examples | # of GPT-4o tokens | # of Gemma 2 tokens | # of Llama 3 tokens |
|-|:-|:-|:-|:-|
| id | 100 | 61628 | 55485 | 77016 |
| ta | 100 | 114275 | 156476 | 457559 |
| th | 100 | 155203 | 151988 | 176985 |
| vi | 100 | 86305 | 78285 | 82269 |
| id_fewshot | 5 | 1124 | 1050 | 1430 |
| ta_fewshot | 5 | 964 | 1339 | 3905 |
| th_fewshot | 5 | 925 | 869 | 1062 |
| vi_fewshot | 5 | 2396 | 2170 | 2282 |
| **total** | 420 | 422820 | 447662 | 802508 |

### Data Sources

| Data Source | License | Language/s | Split/s
|-|:-|:-| :-|
| [XL-Sum](https://huggingface.co/datasets/csebuetnlp/xlsum) | [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) | Indonesian, Tamil, Thai, Vietnamese | id, id_fewshot, ta, ta_fewshot, th, th_fewshot, vi, vi_fewshot

### License

For the license/s of the dataset/s, please refer to the data sources table above.

We endeavor to ensure data used is permissible and have chosen datasets from creators who have processes to exclude copyrighted or disputed data. 


### References

```bibtex
@inproceedings{hasan-etal-2021-xl,
    title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
    author = "Hasan, Tahmid  and
      Bhattacharjee, Abhik  and
      Islam, Md. Saiful  and
      Mubasshir, Kazi  and
      Li, Yuan-Fang  and
      Kang, Yong-Bin  and
      Rahman, M. Sohel  and
      Shahriyar, Rifat",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.413",
    pages = "4693--4703",
}

@misc{leong2023bhasaholisticsoutheastasian,
      title={BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models}, 
      author={Wei Qi Leong and Jian Gang Ngui and Yosephine Susanto and Hamsawardhini Rengarajan and Kengatharaiyer Sarveswaran and William Chandra Tjhi},
      year={2023},
      eprint={2309.06085},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2309.06085}, 
}
```