N24News: A New Dataset for Multimodal News Classification

LREC 2022  ·  Zhen Wang, Xu Shan, Xiangxie Zhang, Jie Yang ·

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
News Classification N15News Multimodal(ViT+BERT, Input: Image + Body) Accuracy 0.9249 # 1
News Classification N15News Multimodal(ViT+BERT, Input: Image + Abstract) Accuracy 0.8610 # 3
News Classification N15News Multimodal(ViT+BERT, Input: Image + Caption) - Concatenate Accuracy 0.7951 # 6
News Classification N15News Multimodal(ViT+BERT, Input: Image + Headline) - Dot Accuracy 0.8202 # 5
News Classification N15News BERT (Input: Body) Accuracy 0.9203 # 2
News Classification N15News BERT (Input: Abstract) Accuracy 0.8471 # 4
News Classification N15News BERT (Input: Caption) Accuracy 0.7792 # 7
News Classification N15News BERT (Input: Headline) Accuracy 0.7727 # 8
News Classification N15News ViT (Input: Image) Accuracy 0.6065 # 9


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