IMPACT OF MINIMUM SPANNING TREE ALGORITHMS ON EXTRACTIVE ARABIC TEXT SUMMARIZATION APPROACH

The purpose of this research is to investigate the impact of using Minimum Spanning Tree (MST) algorithms for enhancing the Arabic Text Summarization (ATS) graph-based approach's performance. The previous researches were conducted in an extractive ATS that relied on a graph approach are very limited, and their performance is still low. This low performance is attributed to the characteristics of Arabic language which is morphologically complex, moreover, there is a lack in ATS researches using graph-based technique. The final results of the graph-based technique mainly rely on the weights between sentences as major features which are poorly calculated. To address these limitations, this study applies and evaluates three MST algorithms (Prim’s, Kruskal’s, and Bourka's) within a single-document extractive ATS system. The proposed system converts text into a graph where sentences are nodes and similarity-based weights are used as edges. The MST algorithm is then applied to extract the most representative sentences. To ensure objective comparison, the Essex Arabic Summaries Corpus (EASC) was used as a benchmark dataset. Experimental results show that Kruskal’s MST algorithm achieves the best performance, demonstrating a significant improvement of 15.2% in recall and 14.3% in F-measure over previous single-document extractive ATS methods. This confirms the effectiveness of MST-based graph algorithms in improving Arabic text summarization quality.