A Hybrid Long Arabic Text Summarization System Based on Integrated Approach Between Abstractive and Extractive

Abstract

Inevitably generating a robust summary from a long Arabic document is a challenging task owing to the fact that Arabic is a complex language and has unique attributes. In this paper, we propose an integrated approach between abstractive and extractive for providing an informative and coherent summary from a long document. The extractive method employs a novel formulation for extracting a set of statistical and semantic features by taking into consideration the semantic, importance, and position of the sentence. The combination of statistical and semantic features is used to learn a soft voting classifier to extract the significant sentences. In the abstractive approach, only significant sentences that classified from the extractive approach will be trained with encoder-decoder bidirectional long short-term memory (LSTM) for producing a compose novel summary. We show that the mixed proposed architecture between extractive and abstractive outperforms and provides better results comparing to some existing Arabic summarizing systems.