Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents

The paper introduces a hierarchical graph convolutional network (HGCN) designed to detect low‑quality documents by identifying internal inconsistencies and mismatches between titles and bodies. The authors created the Inconsistency Dataset from news data and metadata to train the model, achieving 91.20% accuracy on this dataset and 92.00% on a public incongruent dataset. An ablation study shows that incorporating metadata significantly improves performance, suggesting the model’s broad applicability for filtering low‑quality content.

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• nlp
• benchmarks