Utilizing Association Rules in Knowledge Graphs for Enhanced News Summarization

The rapid progress in web news articles has led to an abundance of text content, often than needed, and consequently, misleading readers. Recent Knowledge Graph (KG) based approaches have proven successful in abstract summary generation due to their ability to represent structured and interconnected knowledge with semantic context. The KG ranking algorithm responsible for selecting graph data for inclusion in the abstract still relies on traditional ranking algorithms which lack the consideration for semantic relationships between graph nodes, and are associated with high memory consumption, processing times, and increased complexity. Knowledge discovery plays a crucial role in improving the quality of summarization by uncovering hidden patterns and enhancing contextual understanding. Therefore, our study centers on introducing a novel KG ranking algorithm, aimed at a statistically significant enhancement in abstract generation by integrating knowledge discovery techniques. The suggested ranking algorithm considers the semantic and topological graph properties and interesting relationships, patterns, and features in text data using Association Rule Mining techniques to identify the most significant graph information for generating abstracts. The experiments conducted using the DUC-2002 dataset indicate that the suggested KG ranking algorithm is effective in producing detailed and accurate abstracts for a collection of web news articles.