SciBERT-Based Interpretable Framework for Citation Influence Classification Using Sparse Rationale Extraction

Assessing the influence of scientific citations is critical to understanding the impact of scholarly research beyond the raw citation counts. Recent neural models achieve strong performance in citation classification but typically function as black boxes, limiting their suitability for transparent b...

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Auteurs principaux:	Arshad Iqbal, Muhammad Inam Ul Haq, Abdul Wahid, Shad Muhammad, Muhammad Yahya, Abdul Shahid
Format:	Artigo
Langue:	Inglês
Publié:	IEEE 2026-01-01
Collection:	IEEE Access
Sujets:	Citation importance explainable AI SciBERT sparse rationale extraction HardKuma interpretability
Accès en ligne:	https://ieeexplore.ieee.org/document/11499403/
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