Papers › N24News: A New Dataset for Multimodal News Classification
N24News: A New Dataset for Multimodal News Classification
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.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 24 | #3 of 6 | Archive leaderboard | report | ||||
| News Classification | N15News | Multimodal(ViT+BERT, Input: Image + Body) | Accuracy | 0.9249 | #1 of 9 | Archive leaderboard | report |
| News Classification | N15News | BERT (Input: Body) | Accuracy | 0.9203 | #2 of 9 | Archive leaderboard | report |
| News Classification | N15News | Multimodal(ViT+BERT, Input: Image + Abstract) | Accuracy | 0.8610 | #3 of 9 | Archive leaderboard | report |
| News Classification | N15News | BERT (Input: Abstract) | Accuracy | 0.8471 | #4 of 9 | Archive leaderboard | report |
| News Classification | N15News | Multimodal(ViT+BERT, Input: Image + Headline) - Dot | Accuracy | 0.8202 | #5 of 9 | Archive leaderboard | report |
| News Classification | N15News | Multimodal(ViT+BERT, Input: Image + Caption) - Concatenate | Accuracy | 0.7951 | #6 of 9 | Archive leaderboard | report |
| News Classification | N15News | BERT (Input: Caption) | Accuracy | 0.7792 | #7 of 9 | Archive leaderboard | report |
| News Classification | N15News | BERT (Input: Headline) | Accuracy | 0.7727 | #8 of 9 | Archive leaderboard | report |
| News Classification | N15News | ViT (Input: Image) | Accuracy | 0.6065 | #9 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections