Abstract
► An Arabic text summarization system. ► Integrates Rhetorical Structure Theory (RST) and sentence scoring scheme. ► Users can choose the max size of the summary. An algorithm based on 0/1 Knapsack picks the best set of sentences such that the total score is maximized while at the same time the constraint of summary length is held true. ► Our system is not machine learning based. ► Used ROUGE to evaluate the Arabic summary.
Automatic text summarization is an essential tool in this era of information overloading. In this paper we present an automatic extractive Arabic text summarization system where the user can cap the size of the final summary. It is a direct system where no machine learning is involved. We use a two pass algorithm where in pass one, we produce a primary summary using Rhetorical Structure Theory (RST); this is followed by the second pass where we assign a score to each of the sentences in the primary summary. These scores will help us in generating the final summary. For the final output, sentences are selected with an objective of maximizing the overall score of the summary whose size should not exceed the user selected limit. We used Rouge to evaluate our system generated summaries of various lengths against those done by a (human) news editorial professional. Experiments on sample texts show our system to outperform some of the existing Arabic summarization systems including those that require machine learning.