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---
license: cc-by-nc-sa-4.0
dataset_info:
features:
- name: headline
dtype: string
- name: full_text
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 397400073
num_examples: 127402
- name: test
num_bytes: 33550682
num_examples: 10000
download_size: 280794360
dataset_size: 430950755
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- text-classification
- text-generation
- text-retrieval
- text2text-generation
- sentence-similarity
language:
- it
size_categories:
- 100K<n<1M
---
# CHANGE-IT
Disclaimer: This dataset is not the official CHANGE-IT repository from EVALITA. For the official dataset and more information, please visit the [EVALITA CHANGE-IT page](https://sites.google.com/view/change-it/home) or the [CHANGE-IT repository](https://github.com/michelecafagna26/CHANGE-IT).
## Overview
The CHANGE-IT dataset is designed for a style transfer task focused on headlines from Italian newspapers. The dataset comprises approximately 152,000 article-headline pairs sourced from two prominent Italian newspapers, la Repubblica (left-leaning) and Il Giornale (right-leaning). The data is equally split between the two sources, providing a balanced representation of differing political perspectives. For each article, both the headline and its corresponding article text are included.
## Purpose
The primary objective of the CHANGE-IT dataset is to facilitate research on style transfer between headlines from newspapers with opposing political orientations. Researchers are encouraged to transform headlines from Il Giornale to the style of la Repubblica and vice versa.
## Data Fields
- `headline`: The original headline of the newspaper.
- `full_text`: The article full text associated to the respective headline.
- `source`: The newspaper the sample is coming from, which can be either `repubblica` or `ilgiornale`.
## Citatation
If you use this dataset, please cite the original authors:
```
@article{de2020change,
title={CHANGE-IT@ EVALITA 2020: Change Headlines, Adapt News, GEnerate},
author={De Mattei, Lorenzo and Cafagna, Michele and AI, Aptus and Dell’Orletta, Felice and Nissim, Malvina and Gatt, Albert},
year={2020}
}
``` |