--- 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}, } ```