Transforming Semantic Link Networks into Coherent Multi-Document Summaries

: The growing demand for advanced multi-document summarization necessitates innovative methods to represent and understand document semantics effectively. This paper introduces a framework for abstractive multi-document summarization using Semantic Link Networks (SLNs) to transform and represent document content. The proposed approach constructs an SLN by extracting and connecting key concepts and events from the source documents, creating a semantic structure that captures their interrelations. A coherence-preserving selection mechanism is then applied to identify and summarize the most critical components of the network. Unlike extractive methods that copy content verbatim, our approach generates summaries that are semantically rich and concise, aligning closely with the context of the original documents. Experiments conducted on benchmark datasets, including CNN/Daily Mail dataset, demonstrate that the proposed method achieves an improvement of 10.5% in ROUGE-1 and 12.3% in BLEU scores compared to state-of-the-art baselines. The framework achieves an overall accuracy of 94.8% in semantic coherence and content coverage, significantly outperforming existing methods. These results highlight the potential of SLNs to bridge the gap between document representation and understanding for abstractive summarization tasks. This work advances summarization techniques by offering a novel, effective framework and underscores the promise of SLNs as a robust tool for semantic-based information processing.