metadata

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
  splits:
    - name: train
      num_bytes: 2494253
      num_examples: 700
    - name: validation
      num_bytes: 214922
      num_examples: 50
    - name: test
      num_bytes: 358972
      num_examples: 100
  download_size: 1745629
  dataset_size: 3068147
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

"A Spanish dataset for Clickbait articles summarization"

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
🔌 Online Demo: 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
Language(s) (NLP): Spanish
License: apache-2.0

Dataset Usage

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

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
If you want to manually load the dataset and run inference with an LLM: You can load the dataset with the following command:

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:

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.

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

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, 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 annotated by Iker García-Ferrero and validated by . The annotation took ~40 hours.