--- pretty_name: SEA Abstractive Summarization license: - cc-by-nc-sa-4.0 task_categories: - text-generation language: - id - ta - th - vi dataset_info: - config_name: id features: - name: id dtype: string - name: label dtype: string - name: prompts list: - name: text dtype: string - name: metadata struct: - name: language dtype: string - name: title dtype: string - name: url dtype: string splits: - name: eval num_bytes: 295312 num_examples: 100 - name: examples num_bytes: 6660 num_examples: 5 download_size: 189280 dataset_size: 301972 - config_name: my features: - name: id dtype: string - name: label dtype: string - name: prompts list: - name: text dtype: string - name: metadata struct: - name: language dtype: string - name: title dtype: string splits: - name: eval num_bytes: 791430 num_examples: 100 - name: examples num_bytes: 63370 num_examples: 5 download_size: 288156 dataset_size: 854800 - config_name: ta features: - name: id dtype: string - name: label dtype: string - name: prompts list: - name: text dtype: string - name: metadata struct: - name: language dtype: string - name: title dtype: string - name: url dtype: string splits: - name: eval num_bytes: 1011914 num_examples: 100 - name: examples num_bytes: 11347 num_examples: 5 download_size: 371811 dataset_size: 1023261 - config_name: th features: - name: id dtype: string - name: label dtype: string - name: prompts list: - name: text dtype: string - name: metadata struct: - name: language dtype: string - name: title dtype: string - name: url dtype: string splits: - name: eval num_bytes: 1148394 num_examples: 100 - name: examples num_bytes: 9727 num_examples: 5 download_size: 455457 dataset_size: 1158121 - config_name: tl features: - name: id dtype: string - name: label dtype: string - name: prompts list: - name: text dtype: string - name: metadata struct: - name: language dtype: string - name: title dtype: string - name: url dtype: string splits: - name: eval num_bytes: 89405 num_examples: 100 - name: examples num_bytes: 4159 num_examples: 5 download_size: 67523 dataset_size: 93564 - config_name: vi features: - name: id dtype: string - name: label dtype: string - name: prompts list: - name: text dtype: string - name: metadata struct: - name: language dtype: string - name: title dtype: string - name: url dtype: string splits: - name: eval num_bytes: 368697 num_examples: 100 - name: examples num_bytes: 9736 num_examples: 5 download_size: 226848 dataset_size: 378433 configs: - config_name: id data_files: - split: eval path: id/eval-* - split: examples path: id/examples-* - config_name: my data_files: - split: eval path: my/eval-* - split: examples path: my/examples-* - config_name: ta data_files: - split: eval path: ta/eval-* - split: examples path: ta/examples-* - config_name: th data_files: - split: eval path: th/eval-* - split: examples path: th/examples-* - config_name: tl data_files: - split: eval path: tl/eval-* - split: examples path: tl/examples-* - config_name: vi data_files: - split: eval path: vi/eval-* - split: examples path: vi/examples-* 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. ## Acknowledgement This project is supported by the National Research Foundation Singapore and Infocomm Media Development Authority (IMDA), Singapore under its National Large Language Model Funding Initiative. ### 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}, } ```