Knowledge-Enhanced Transformer Graph Summarization (KETGS): Integrating Entity and Discourse Relations for Advanced Extractive Text Summarization

The rapid proliferation of textual data across multiple sectors demands more sophisticated and efficient techniques for summarizing extensive texts. Focusing on extractive text summarization, this approach zeroes in on choosing key sentences from a document, providing an essential method for handling extensive information. While conventional methods often miss capturing deep semantic links within texts, resulting in summaries that might lack cohesion and depth, this paper introduces a novel framework called Knowledge-Enhanced Transformer Graph Summary (KETGS). Leveraging the strengths of both transformer models and Graph Neural Networks, KETGS develops a detailed graph representation of documents, embedding linguistic units from words to key entities. This structured graph is then navigated via a Transformer-Guided Graph Neural Network (TG-GNN), dynamically enhancing node features with structural connections and transformer-driven attention mechanisms. The framework adopts a Maximum Marginal Relevance (MMR) strategy for selecting sentences. Our evaluations show that KETGS outshines other leading extractive summarization models, delivering summaries that are more relevant, cohesive, and concise, thus better preserving the essence and structure of the original texts.