Extractive Arabic Text Summarization-Graph-Based Approach

With the noteworthy expansion of textual data sources in recent years, easy, quick, and precise text processing has become a challenge for key qualifiers. Automatic summarization is process squeezing documents into shorter summaries to facilitate verification their basic contents, which must be completed without losing vital information features. The most difficult retrieval task summarization, particularly Arabic. In this research, we offer an automatic, general, extractive Arabic single document summarizing approach with goal delivering sufficiently informative summary. proposed model based on graph generate coherent Firstly, original converted using novel formulation that takes account sentence relevance, coverage, diversity evaluate each mix statistical semantic criteria. Next, sub-graph built reduce size text. Finally, unwanted less weighted phrases are removed from summarized sentences final We used Recall-Oriented Research Evaluate Main Idea (RED) as evaluative metric review our technique compare it advanced methods. trial Essex Summary Corpus (EASC) ROUGE index showed promising results compared currently available