Reinforced Hybrid Learning Model for Context-Sensitive Abstractive Summarization across Document Clusters

Abstract

Abstractive summarization of document clusters presents a complex challenge due to heterogeneous content, varying narrative styles, and the need for context-sensitive coherence. Traditional approaches often rely exclusively on either supervised learning, which requires substantial labeled datasets but generates fluent summaries, or unsupervised methods, which adapt to new data but may lack precision and coherence. This paper introduces a reinforced hybrid learning model that combines supervised and unsupervised paradigms with reinforcement learning to optimize context-sensitive abstractive summarization across document clusters. The model leverages supervised guidance for grammatical correctness and semantic alignment, unsupervised representations for salient information extraction, and reinforcement signals to reward contextually coherent and concise summaries. Evaluation on benchmark datasets demonstrates improvements in ROUGE, BERTScore, and human-assessed coherence compared to conventional models. The findings suggest that integrating hybrid learning with reinforcement mechanisms enhances both semantic coverage and readability, offering a robust approach for real-world applications such as news aggregation, research literature synthesis, and business intelligence.