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 Usage

# 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, 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⚔️, 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 and has been validated by Begoña Altura. The annotation took ~40 hours.

Citation

@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}
}