Datasets:
license: mit
task_categories:
- summarization
language:
- pt
tags:
- pt
- pt-br
- summarization
- abstractive summarization
- news
pretty_name: RecognaSumm
size_categories:
- 100K<n<1M
RecognaSumm Dataset
Introduction
RecognaSumm is a novel and comprehensive database specifically designed for the task of automatic text summarization in Portuguese. RecognaSumm stands out due to its diverse origin, composed of news collected from a variety of information sources, including agencies and online news portals. The database was constructed using web scraping techniques and careful curation, re sulting in a rich and representative collection of documents covering various topics and journalis tic styles. The creation of RecognaSumm aims to fill a significant void in Portuguese language summarization research, providing a training and evaluation foundation that can be used for the development and enhancement of automated summarization models.
News Categories
Category | # of news |
---|---|
Brazil | 14,131 |
Economy | 12,613 |
Entertainment | 5,337 |
Health | 24,921 |
Policy | 29,909 |
Science and Technology | 15,135 |
Sports | 2,915 |
Travel and Gastronomy | 2,893 |
World | 27,418 |
Total | 135,272 |
PTT5-Summ Model
We also trained the PTT5 model on this dataset and made it available on HuggingFace. Click here to access.
Citation
RecognaSumm: A Novel Brazilian Summarization Dataset (PROPOR 2024)
@inproceedings{paiola-etal-2024-recognasumm,
title = "{R}ecogna{S}umm: A Novel {B}razilian Summarization Dataset",
author = "Paiola, Pedro Henrique and
Garcia, Gabriel Lino and
Jodas, Danilo Samuel and
Correia, Jo{\~a}o Vitor Mariano and
Sugi, Luis Afonso and
Papa, Jo{\~a}o Paulo",
editor = "Gamallo, Pablo and
Claro, Daniela and
Teixeira, Ant{\'o}nio and
Real, Livy and
Garcia, Marcos and
Oliveira, Hugo Gon{\c{c}}alo and
Amaro, Raquel",
booktitle = "Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 1",
month = mar,
year = "2024",
address = "Santiago de Compostela, Galicia/Spain",
publisher = "Association for Computational Lingustics",
url = "https://aclanthology.org/2024.propor-1.63",
pages = "575--579",
}