---
language:
- es
license: apache-2.0
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
  - name: clean_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 Spanish dataset for Clickbait articles summarization"</h3>

This repository contains the manual annotations from a second human in order 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. 
- **web_text** (int):  The body of the article. 
- **clean_web_text** (str): The `web_text` has been downloaded from the web HTML and can contain undesired text not related to the news article. The `clean_web_text` has been cleaned using the OpenAI gpt-3.5-turbo-0125 model. We ask the model to remove any sentence unrelated to the article. 
- **summary** (str):  The original summary in the NoticIA dataset.
- **summary2** (str): The second summary written by another human to validate the quality of `summary`
- 

# 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
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.

# 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 originaly 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.