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New HGCN model accurately detects low-quality documents

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a hierarchical graph convolutional network (HGCN) designed to automatically detect low-quality documents by identifying internal inconsistencies and discrepancies between a document's title and body. The model was trained on a newly constructed "Inconsistency Dataset" derived from published news data. Experiments showed the HGCN achieved 91.20% accuracy on this dataset and 92.00% on a public dataset, outperforming other comparative models. The utilization of metadata was also found to significantly enhance the model's performance. AI

IMPACT This model could improve the reliability of information by filtering out inconsistent or misleading content.

RANK_REASON The cluster contains a research paper detailing a new model and dataset for detecting low-quality documents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New HGCN model accurately detects low-quality documents

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The cluster contains a research paper detailing a new model and dataset for detecting low-quality documents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jaeyoung Lee, Joonwon Jang, Misuk Kim ·

    Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents

    arXiv:2610.21350v1 Announce Type: new Abstract: Consistency within a document is a crucial feature indicative of its quality. Recently, within the vast amount of information produced across various media, there exists a significant number of low-quality documents that either lack…

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