Abstract
The growing volume of digital text content requires automated summarization systems as fundamental tools that enable users to access information at high speed. Automatic Multi-Document Summarization (MDS) requires systems to produce a summary that combines essential information from multiple documents. The extractive methods, which rely on lexical signals and sentence-based rules, yield only repetitive results because they cannot capture complex thematic relationships. This study developed an improved extractive MDS model that combines topic modeling with pattern-based semantic indicators and a method to choose diverse sentences. The model employs LDA to identify concealed thematic structures, retrieves typical word patterns to improve topic models, and selects topics via a greedy algorithm that reduces redundancy to achieve appropriate salience and coverage. The proposed system achieves better results than classical baselines on the DUC 2006 and DUC 2007 datasets, outperforming Lead, CLASSY04, KL-SUM, LexRank, TextRank, and PETMSUM. The system demonstrates superior performance over all baseline methods, achieving better results on the ROUGE-1, ROUGE-2, and ROUGE-SU4 evaluation metrics. The results show that extractive summarization tasks achieve their best performance when topic–pattern representations are combined with diversity-aware scoring methods.