README.md

metadata

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 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 leaderboard from AI Singapore.

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	CC 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

@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}, 
}