Semantic-Aware Hybrid Text Summarization Using Supervised Sentence Scoring and Redundancy Control

Abstract

The rapid growth of digital textual content has intensified the need for automatic text summarization sys- tems that are both effective and reliable. While extractive summarization methods are interpretable and preserve factual content, they often suffer from redundancy and limited coherence. In contrast, abstractive approaches based on large pretrained transformer models improve fluency and readability but are prone to factual inconsistencies and hallucination. To address these limitations, this paper proposes a semantic- aware hybrid text summarization framework that integrates supervised extractive sentence scoring with constrained abstractive generation. The proposed approach employs an interpretable sentence importance model based on lexical, positional, and semantic features, learned using a Gradient Boosting Regressor. Semantic redundancy among candidate sentences is explicitly controlled using Word Mover’s Distance, enabling improved content diversity without increasing training complexity. The selected sentences are subsequently refined using a transformer-based abstractive model to enhance coherence and linguistic quality while preserving factual consistency. The framework is evaluated on a benchmark news summa- rization dataset using ROUGE-1, ROUGE-2, and ROUGE-L metrics. Experimental results demonstrate consistent improvements over a purely extractive baseline and competitive performance compared to repre- sentative extractive, abstractive, and hybrid approaches. Ablation studies further confirm the contribution of supervised sentence scoring, semantic redundancy control, and abstractive refinement to performance stability. Overall, the results indicate that combining interpretable sentence selection with semantic simi- larity modeling within a hybrid architecture provides a balanced and practical solution for automatic text summarization.