A Hybrid Long Arabic Text Summarization System Based on Integrated Approach Between Abstractive and Extractive
6th International Conference on Computer and Technology Applications, ICCTA 2020, Antalya, Turkey, 14 - 16 April 2020, pp.109-114, (Full Text)
- Publication Type: Conference Paper / Full Text
- Doi Number: 10.1145/3397125.3397129
- City: Antalya
- Country: Turkey
- Page Numbers: pp.109-114
- Keywords: Abstractive, Extractive, K@Bidirectional-LSTM; NLP, Summary
- Yıldız Technical University Affiliated: Yes
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.