TITLE: MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification 

ABSTRACT: This article presents a dataset of 10,917 news articles with hierarchical
news categories collected between 1 January 2019 and 31 December 2019. We
manually labeled the articles based on a hierarchical taxonomy with 17
first-level and 109 second-level categories. This dataset can be used to train
machine learning models for automatically classifying news articles by topic.
This dataset can be helpful for researchers working on news structuring,
classification, and predicting future events based on released news.
This article presents a dataset of 10,917 news articles with hierarchical
news categories collected between 1 January 2019 and 31 December 2019. We
manually labeled the articles based on a hierarchical taxonomy with 17
first-level and 109 second-level categories. This dataset can be used to train
machine learning models for automatically classifying news articles by topic.
This dataset can be helpful for researchers working on news structuring,
classification, and predicting future events based on released news.
MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification
http://arxiv.org/abs/2212.12061v3