TinyReview: An Abstract Prediction and MetaReview Generation

"smart abstract evaluator" is an intelligent ai-powered framework designed to automatically assess and justify the acceptance potential of research abstracts, addressing the challenges of subjective peer evaluation and inconsistent review quality in academic submissions. Unlike existing systems that merely classify papers based on predefined keywords or citation metrics, the proposed model integrates both interpretability and analytical depth within a unified architecture. The framework combines a tinybert-based classifier for acceptance prediction with an mlp layer to assess the technical depth and coherence of the abstract, ensuring a holistic evaluation of both content quality and scientific rigor. Additionally, a novelty-checking module powered by the arxiv api quantifies the uniqueness of the submission by comparing semantic similarity with existing research works. To enhance transparency, a t5-based justification generator produces concise, human-readable explanations for each decision, bridging the gap between prediction and interpretability. The system achieves high accuracy while maintaining explainability, outperforming traditional models such as distilbert in both precision and recall. By integrating natural language understanding, novelty detection, and justification generation, smart abstract evaluator contributes to the automation of academic paper screening, providing a scalable, unbiased, and interpretable alternative to the manual peer-review process.