Datasets:
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---
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
- name: id_fewshot
num_bytes: 5963
num_examples: 5
- name: ta
num_bytes: 1075514
num_examples: 100
- name: ta_fewshot
num_bytes: 10198
num_examples: 5
- name: th
num_bytes: 1201794
num_examples: 100
- name: th_fewshot
num_bytes: 8735
num_examples: 5
- name: vi
num_bytes: 395697
num_examples: 100
- name: vi_fewshot
num_bytes: 9092
num_examples: 5
download_size: 1258846
dataset_size: 3029105
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},
}
``` |