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---
language:
- es
license: cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- summarization
pretty_name: NoticIA Human Validation
dataset_info:
features:
- name: web_url
dtype: string
- name: web_headline
dtype: string
- name: summary
dtype: string
- name: summary2
dtype: string
- name: web_text
dtype: string
splits:
- name: test
num_examples: 100
configs:
- config_name: default
data_files:
- split: test
path: test.jsonl
tags:
- summarization
- clickbait
- news
---
<p align="center">
<img src="https://huggingface.co/datasets/Iker/NoticIA/resolve/main/assets/logo.png" style="height: 250px;">
</p>
<h3 align="center">"A Clickbait Article Summarization Dataset in Spanish."</h3>
This repository contains the manual annotations from a second human to validate the test set of the NoticIA dataset.
The full NoticIA dataset is available here: [https://huggingface.co/datasets/Iker/NoticIA](https://huggingface.co/datasets/Iker/NoticIA)
# Data explanation
- **web_url** (int): The URL of the news article
- **web_headline** (str): The headline of the article, which is a Clickbait.
- **summary** (str): The original summary in the NoticIA dataset.
- **summary2** (str): The second summary written by another human to validate the quality of `summary`
- **web_text** (int): The body of the article.
# Dataset Description
- **Curated by:** [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/), [Begoña Altura](https://www.linkedin.com/in/bego%C3%B1a-altuna-78014139)
- **Language(s) (NLP):** Spanish
- **License:** apache-2.0
# Dataset Usage
```Python
# pip install datasets evaluate rouge-score
from datasets import load_dataset
from evaluate import load
dataset = load_dataset("Iker/NoticIA_Human_Validation",split="test")
rouge = load("rouge")
results = rouge.compute(
predictions=[x["summary2"] for x in dataset],
references=[[x["summary"]] for x in dataset],
use_aggregator=True,
)
print(results)
```
# Uses
This dataset is intended to build models tailored for academic research that can extract information from large texts. The objective is to research whether current LLMs, given a question formulated as a Clickbait headline, can locate the answer within the article body and summarize the information in a few words. The dataset also aims to serve as a task to evaluate the performance of current LLMs in Spanish.
# Out-of-Scope Use
You cannot use this dataset to develop systems that directly harm the newspapers included in the dataset. This includes using the dataset to train profit-oriented LLMs capable of generating articles from a short text or headline, as well as developing profit-oriented bots that automatically summarize articles without the permission of the article's owner. Additionally, you are not permitted to train a system with this dataset that generates clickbait headlines.
This dataset contains text and headlines from newspapers; therefore, you cannot use it for commercial purposes unless you have the license for the data.
# Dataset Creation
The dataset has been meticulously created by hand. We utilize two sources to compile Clickbait articles:
- The Twitter user [@ahorrandoclick1](https://twitter.com/ahorrandoclick1), who reposts Clickbait articles along with a hand-crafted summary. Although we use their summaries as a reference, most of them have been rewritten (750 examples from this source).
- The web demo [⚔️ClickbaitFighter⚔️](https://iker-clickbaitfighter.hf.space/), which operates a pre-trained model using an early iteration of our dataset. We collect all the model inputs/outputs and manually correct them (100 examples from this source).
# Who are the annotators?
The dataset was originally by [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/) and has been validated by [Begoña Altura](https://www.linkedin.com/in/bego%C3%B1a-altuna-78014139).
The annotation took ~40 hours.
# Citation
```bittext
@misc{noticia2024,
title={NoticIA: A Clickbait Article Summarization Dataset in Spanish},
author={Iker García-Ferrero and Begoña Altuna},
year={2024},
eprint={2404.07611},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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