---
language:
- es
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- summarization
pretty_name: NoticIA
dataset_info:
  features:
  - name: web_url
    dtype: string
  - name: web_headline
    dtype: string
  - name: summary
    dtype: string
  - name: web_text
    dtype: string
  - name: clean_web_text
    dtype: string
  splits:
  - name: train
    num_bytes: 3954232
    num_examples: 703
  - name: validation
    num_bytes: 352363
    num_examples: 50
  - name: test
    num_bytes: 603004
    num_examples: 100
  download_size: 2872679
  dataset_size: 4909599
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
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>

We introduce a dataset that contains articles with clickbait headlines. We provide the clickbait headline for the article, the corresponding web text, and the summary. The summaries are written by humans and aim to answer the clickbait headlines using the fewest words possible. 


- 📖 Paper: [Coming soon]()
- 💻 Baseline Code: [https://github.com/ikergarcia1996/NoticIA](https://github.com/ikergarcia1996/NoticIA)
- 🔌 Online Demo: [https://iker-clickbaitfighter.hf.space/](https://iker-clickbaitfighter.hf.space/)

For example, given the following headline and web text:
```
# ¿Qué pasará el 15 de enero de 2024?
Al parecer, no todo es dulzura en las vacaciones de fin de años, como lo demuestra la nueva intrig....
```
The summary is:
```
Que los estudiantes vuelven a clase.
```


# 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 summary written by humans that answers the clickbait headline. 

# Dataset Description
- **Curated by:** [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/)
- **Language(s) (NLP):** Spanish
- **License:** apache-2.0

# Dataset Usage

1. The easiest way to evaluate an LLM with this dataset if using the Language Model Evaluation Harness library: https://github.com/EleutherAI/lm-evaluation-harness

```bash
```

2. If you want to train an LLM or reproduce the results in our paper, you can use our code. See the repository for more info: [https://github.com/ikergarcia1996/NoticIA](https://github.com/ikergarcia1996/NoticIA)

3. If you want to manually load the dataset and run inference with an LLM: 
You can load the dataset with the following command:
```Python
from datasets import load_dataset
dataset = load_dataset("Iker/NoticIA")
```

In order to perform inference with LLMs, you need to build a prompt. The one we use in our paper is:
```Python
def clickbait_prompt(
    headline: str,
    body: str,
) -> str:
    """
    Generate the prompt for the model.
    Args:
        headline (`str`):
            The headline of the article.
        body (`str`):
            The body of the article.
    Returns:
        `str`: The formatted prompt.
    """
    return (
        f"Ahora eres una Inteligencia Artificial experta en desmontar titulares sensacionalistas o clickbait. "
        f"Tu tarea consiste en analizar noticias con titulares sensacionalistas y "
        f"generar un resumen de una sola frase que revele la verdad detrás del titular.\n"
        f"Este es el titular de la noticia: {headline}\n"
        f"El titular plantea una pregunta o proporciona información incompleta. "
        f"Debes buscar en el cuerpo de la noticia una frase que responda lo que se sugiere en el título. "
        f"Responde siempre que puedas parafraseando el texto original. "
        f"Usa siempre las mínimas palabras posibles. "
        f"Recuerda responder siempre en Español.\n"
        f"Este es el cuerpo de la noticia:\n"
        f"{body}\n"
    )
```

Here is a practical end-to-end example using the text generation pipeline.
```python
from transformers import pipeline
from datasets import load_dataset

generator = pipeline(model="google/gemma-2b-it",device_map="auto")
dataset = load_dataset("Iker/NoticIA")

example = dataset["test"][0]
prompt = clickbait_prompt(headline=example["web_headline"],body=example["web_text"])
outputs = generator(prompt, return_full_text=False,max_length=4096)
print(outputs)

# [{'generated_text': 'La tuitera ha recibido un número considerable de comentarios y mensajes de apoyo.'}]
```

Here is a practical end-to-end example using the generate function
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from datasets import load_dataset

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it",device_map="auto",quantization_config={"load_in_4bit": True})
dataset = load_dataset("Iker/NoticIA")

example = dataset["test"][0]
prompt = clickbait_prompt(headline=example["web_headline"],body=example["web_text"])
prompt = tokenizer.apply_chat_template(
        [{"role": "user", "content": prompt}],
        tokenize=False,
        add_generation_prompt=True,
    )
model_inputs = tokenizer(
        text=prompt,
        max_length=3096,
        truncation=True,
        padding=False,
        return_tensors="pt",
        add_special_tokens=False,
    )

outputs = model.generate(**model_inputs,max_length=4096)
output_text = tokenizer.batch_decode(outputs)

print(output_text[0])

# La usuaria ha comprado un abrigo para su abuela de 97 años, pero la "yaya" no está de acuerdo.
```


# 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 annotated by [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/) and validated by .
The annotation took ~40 hours.