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About this paper
Yiru Wang, Shen Huang, Gongfu Li, Qiang Deng, Dongliang Liao, Pengda Si, Yujiu Yang, Jin Xu
Researchers from Tencent's WeChat Data Quality Team and Tsinghua Shenzhen International Graduate School developed the Cognitive Quality Assessment Network (CoQAN), a framework that evaluates self-media article quality by modeling layout organization, writing characteristics, and text semantics. The CoQAN model demonstrated superior performance against existing baseline methods in assessing real-world self-media content.
Key insights
- Human perception of article quality involves multiple cognitive processes: surface (layout), shallow (writing characteristics), and deep (text semantics).
- Integrating visual layout, writing style features, and deep text semantics is crucial for accurately assessing the quality of diverse self-media articles, where traditional text-only methods fall short.
- Attention mechanisms within the quality assessment model can provide interpretable insights into which article features (e.g., text length, image quality, title characteristics, content organization) are most influential for perceived quality.
Summary by alphaXiv.
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