Datasets:

Iker
/

NoticIA

	---
	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
	---

	<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>

	We present NoticIA, a dataset consisting of 850 Spanish news articles featuring prominent clickbait headlines, each paired with high-quality, single-sentence generative summarizations written by humans.

	- 📖 Paper: [NoticIA: A Clickbait Article Summarization Dataset in Spanish](https://arxiv.org/abs/2404.07611)
	- 💻 Baseline Code: [https://github.com/ikergarcia1996/NoticIA](https://github.com/ikergarcia1996/NoticIA)
	- 🤖 Pre Trained Models [https://huggingface.co/collections/Iker/noticia-and-clickbaitfighter-65fdb2f80c34d7c063d3e48e](https://huggingface.co/collections/Iker/noticia-and-clickbaitfighter-65fdb2f80c34d7c063d3e48e)
	- 🔌 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.
	- summary (str): The summary written by humans that answers the clickbait headline.

	# Dataset Description
	- Author: [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/)
	- Author [Begoña Altuna](https://www.linkedin.com/in/bego%C3%B1a-altuna-78014139)
	- Web Page: [Github](https://github.com/ikergarcia1996/NoticIA)
	- Language(s) (NLP): Spanish
	- License: apache-2.0

	# Dataset Usage

	1. We are working on implementing NoticIA on the Language Model Evaluation Harness library: https://github.com/EleutherAI/lm-evaluation-harness

	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.

	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 annotated by [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/) and validated by [Begoña Altuna](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}
	}
	```