Low-quality documents are a growing problem as information proliferates across media. A new arXiv paper (arXiv:2610.21350) argues that one reliable marker of quality is internal consistency: whether the parts of a document cohere into a whole. The authors propose a hierarchical graph convolutional network designed to detect such low-quality documents by capturing this consistency.
According to the abstract, the approach is motivated by the observation that consistency is a crucial feature of document quality. The paper's specific architecture and experimental results are not detailed in the abstract, so the claims here are limited to the stated motivation and proposed method.
The significance of the work lies in treating consistency as a computable signal rather than a purely subjective judgment. If the method holds up, it could offer a scalable way to filter the large volume of low-quality content produced across different media.