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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Autori principali:	Arshad Iqbal, Muhammad Inam Ul Haq, Abdul Wahid, Shad Muhammad, Muhammad Yahya, Abdul Shahid
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	IEEE 2026-01-01
Serie:	IEEE Access
Soggetti:	Citation importance explainable AI SciBERT sparse rationale extraction HardKuma interpretability
Accesso online:	https://ieeexplore.ieee.org/document/11499403/
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