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Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents — arXiv:2610.21350

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.21350
License (as archived): CC0-1.0
Archived: 2026-10-16T09:03:07.938Z
SHA-256 of the archived text: 5ba987fe5ae724082c2ab400dce7a86e7b464ebfeaeeb6d3d2c1e878401b803e
Stable URL of this page: https://metraipolis.com/documento/dc_92dfcb026703

How to cite

Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents — arXiv:2610.21350 — arXiv (en), https://arxiv.org/abs/2610.21350 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_92dfcb026703 (Metraipolis archive dc_92dfcb026703, sha256 5ba987fe5ae724082c2ab400dce7a86e7b464ebfeaeeb6d3d2c1e878401b803e)

BibTeX

@misc{dc92dfcb026703,
  title = {Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents — arXiv:2610.21350},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.21350},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_92dfcb026703; sha256 5ba987fe5ae724082c2ab400dce7a86e7b464ebfeaeeb6d3d2c1e878401b803e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.21350. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 internal consistency or contain content utterly unrelated to their headlines. Such low-quality documents induce fatigue in readers and undermine the credibility of the media source that provided them. Consequently, research to automatically detect these low-quality documents based on natural language processing is imperative. In this study, we introduce a hierarchical graph convolutional network (HGCN) that can detect internal inconsistencies within a document and incongruences between the title and body. Moreover, we constructed the Inconsistency Dataset, leveraging published news data and its meta-data, to train our model to detect document inconsistencies. Experimental results demonstrated that the HGCN achieved superior performance with an accuracy of 91.20% on our constructedInconsistency Dataset, outperforming other comparative models. Additionally, on the publicly available incongruent-related dataset, the proposed methodology demonstrated a performance of 92.00%, validating its general applicability. Finally, an ablation study further confirmed the significant impact of meta-data utilization on performance enhancement. We anticipate that our model can be universally applied to detect and filter low-quality documents in the real world.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.21350 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T08:44:17.733Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).