An Approach to Validate References in Scholarly Articles using RoBERTa

The significant research contribution of this paper lies in proposing a semi-automatic digital system for validating references in scholarly articles using RoBERTa-based semantic similarity analysis.

Key contributions include:

Introducing a novel framework that leverages RoBERTa embeddings with K-similar search to verify references against cited works.

Overcoming BERT’s input length limitations by applying document segmentation and preprocessing strategies for handling long research papers.

Achieving higher accuracy (F1-score: 0.777) compared to BERT and SBERT, demonstrating the effectiveness of RoBERTa for contextual similarity in reference validation.

Reducing reliance on manual cross-checking and peer reviewers, thereby streamlining the academic publication process while preserving reference authenticity.