README.md

metadata

language:
  - bo
  - mn
  - ug
license: cc-by-4.0
dataset_info:
  features:
    - name: id
      dtype: string
    - name: title
      dtype: string
    - name: content
      dtype: string
    - name: title_match_1
      dtype: float64
    - name: title_match_2
      dtype: float64
    - name: tendency
      dtype: string
    - name: average_score
      dtype: float64
    - name: score_difference
      dtype: float64
  splits:
    - name: train
      num_bytes: 1415800000
      num_examples: 200000
  task_categories:
    - text-generation
  task_ids:
    - headline-generation

CMHG Dataset

Paper

Dataset Description

The CMHG (Chinese Minority Headline Generation) dataset contains headline generation data for three minority languages in China:

• Tibetan: 100,000 entries
• Mongolian: 50,000 entries
• Uyghur: 50,000 entries

This dataset is designed to support research and development in headline generation for these languages, providing a valuable resource for natural language processing tasks in low-resource languages.

Annotation Process

For quality control, we annotated 3,000 entries for each language. Each entry was evaluated by two annotators who provided scores for the following attributes:

• title_match_1: First annotator's assessment of title-content relevance
• title_match_2: Second annotator's assessment of title-content relevance
• tendency: Sentiment or tendency classification
• average_score: Average score from both annotators
• score_difference: Difference between the two annotators' scores

Data Quality Classification

Based on the annotation results, we classified the data into two quality categories:

• High-quality data: Entries with an average score of 4 or higher (average_score_4_or_higher.csv)
• Lower-quality data: Entries with an average score below 4 (average_score_below_4.csv)

This classification helps researchers and developers select appropriate data for their specific use cases, ensuring they work with data that meets their quality requirements.

Directory Structure

The dataset is organized into language-specific directories:

• bo/: Tibetan language data

  • average_score_4_or_higher.csv: High-quality Tibetan data
  • average_score_below_4.csv: Lower-quality Tibetan data
  • bo-all.csv: Complete Tibetan dataset

• mn/: Mongolian language data

  • average_score_4_or_higher.csv: High-quality Mongolian data
  • average_score_below_4.csv: Lower-quality Mongolian data
  • mn-all.csv: Complete Mongolian dataset

• ug/: Uyghur language data

  • average_score_4_or_higher.csv: High-quality Uyghur data
  • average_score_below_4.csv: Lower-quality Uyghur data
  • ug-3.csv: Complete Uyghur dataset

Data Format

All CSV files follow the same structure with these columns:

• id: Unique identifier for each entry
• title: Generated headline
• content: Original content/text
• title_match_1: First annotator's relevance score
• title_match_2: Second annotator's relevance score
• tendency: Sentiment/tendency label
• average_score: Average quality score
• score_difference: Difference between annotator scores

License

This dataset is available under the CC BY 4.0 license for research and development purposes.

Citation

If you use this dataset in your work, please cite the following paper:

@inproceedings{xu-etal-2025-cmhg,
    title = "{CMHG}: A Dataset and Benchmark for Headline Generation of Minority Languages in {C}hina",
    author = "Xu, Guixian  and
      Su, Zeli  and
      Zhang, Ziyin  and
      Liu, Jianing  and
      Han, Xu  and
      Zhang, Ting  and
      Dong, Yushuang",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.622/",
    doi = "10.18653/v1/2025.emnlp-main.622",
    pages = "12350--12357",
    ISBN = "979-8-89176-332-6"
}