Improve the Automatic Summarization of Arabic Text Depending on Rhetorical Structure Theory

This paper uses a semantic technique by adopting a Rhetorical Structure Theory (RST) for summarization purpose, to discover the most significant paragraphs based on functional and semantic criteria. However, the quality of RST summarization suffers when dealing with large documents. This paper proposes a new hybrid summarization model for Arabic text, which mingles two sub-models: The first sub-model produces a primary summary by using Rhetorical Structure Theory for identifying a range of the most significant parts of the text (the nucleus). Then the second sub-model ranks the significant parts in the primary rhetorical-summary based on the cosine similarity feature. To evaluate the proposed model, a prototype was developed on a range of articles, which have been classified into three groups different in size. The final output summary was evaluated in relation to its manual counterpart. In terms of enhancement of the rhetorical-summary precision, the experiment shows that proposed model HSM average precision is 71.6%, superior over the primary rhetorical-summary precision 56.3%.