license: mit
language:
- fr
annotations_creators:
- expert-generated
size_categories:
- n<3k
source_datasets:
- Grand Débat National
task_categories:
- text-classification
- text-generation
configs:
- config_name: default
data_files:
- split: train
path: GDNCC_data_train.jsonl
- split: test
path: GDNCC_data_test.jsonl
- split: eval
path: GDNCC_data_valid.jsonl
- config_name: AU_detection
data_files:
- split: train
path: GDNCC_AU_detection_train.jsonl
- split: test
path: GDNCC_AU_detection_test.jsonl
- split: eval
path: GDNCC_AU_detection_valid.jsonl
- split: corpus
path: GDNCC_AU_detection.jsonl
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
@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}
}