---
license: mit
language:
- fr
annotations_creators:
- expert-generated
size_categories:
- n<3k
source_datasets:
- Grand Débat National
task_categories:
- text-classification
- text-generation
configs:  # Optional. This can be used to pass additional parameters to the dataset loader, such as `data_files`, `data_dir`, and any builder-specific parameters  
- config_name: default  # Name of the dataset subset, if applicable. Example: default
  data_files:
  - split: train  # Example: train
    path: GDNCC_data_train.jsonl  # Example: data.csv
  - split: test # Example: test
    path: GDNCC_data_test.jsonl   # Example: holdout.csv
  - split: eval # Example: test
    path: GDNCC_data_valid.jsonl   # Example: holdout.csv
- config_name: AU_detection  # Name of the dataset subset. Example: processed
  data_files:
  - split: train  # Example: train
    path: GDNCC_AU_detection_train.jsonl  # Example: data.csv
  - split: test # Example: test
    path: GDNCC_AU_detection_test.jsonl   # Example: holdout.csv
  - split: eval # Example: test
    path: GDNCC_AU_detection_valid.jsonl   # Example: holdout.csv
  - split: corpus  # Example: train
    path: GDNCC_AU_detection.jsonl  # Example: data.csv
---

# Dataset Card for GDN-CC

GDN-CC, short for **Grand Debat National - Corpus Clarification** is a manually annotated dataset for the task of **Corpus Clarification**, introduced in *The GDN-CC Dataset: Automatic Corpus Clarification for AI-enhanced
Democratic Citizen Consultations, Lequeu et al. 2026*. The Corpus Clarification task is preprocessing framework for large-scale consultation data that transforms noisy, multi-topic contributions into structured, self-contained argumentative units ready for downstream analysis.
It is comprised of a three-task pipeline: Argumentative Unit Extraction, Argumentative Structure detection and Argumentaticz Unit Segmentation. 

This process was applied to 1231 contribution to the French citizen consultations "**Grand Debat National**", making up 2285 unique argumentative units. 
splits are provided for comparisons with the original work.


## Citation 
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

```bibtex
@article{lequeu2026gdn,
  title={The GDN-CC Dataset: Automatic Corpus Clarification for AI-enhanced Democratic Citizen Consultations},
  author={Lequeu, Pierre-Antoine and Labat, L{\'e}o and Cave, Laur{\`e}ne and Lejeune, Ga{\"e}l and Yvon, Fran{\c{c}}ois and Piwowarski, Benjamin},
  journal={arXiv preprint arXiv:2601.14944},
  year={2026}
}
```