  Current news datasets merely focus on text features on the news and rarely
leverage the feature of images, excluding numerous essential features for news
classification. In this paper, we propose a new dataset, N24News, which is
generated from New York Times with 24 categories and contains both text and
image information in each news. We use a multitask multimodal method and the
experimental results show multimodal news classification performs better than
text-only news classification. Depending on the length of the text, the
classification accuracy can be increased by up to 8.11%. Our research reveals
the relationship between the performance of a multimodal classifier and its
sub-classifiers, and also the possible improvements when applying multimodal in
news classification. N24News is shown to have great potential to prompt the
multimodal news studies.
