Using Statistical and Semantic Analysis for Arabic Text Summarization

2017 ◽  
pp. 35-50 ◽  
Author(s):  
Nabil Alami ◽  
Yassine El Adlouni ◽  
Noureddine En-nahnahi ◽  
Mohammed Meknassi
Keyword(s):  
Semantic Analysis ◽  
Text Summarization ◽  
Arabic Text ◽  
Arabic Text Summarization

Arabic Text Summarization Using Latent Semantic Analysis

2015 ◽  
Vol 10 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Fadl Ba-Alwi ◽  
Ghaleb Gaphari ◽  
Fares Al-Duqaimi
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Text Summarization ◽  
Arabic Text ◽  
Arabic Text Summarization

scholarly journals Arabic text summarization via Knapsack balancing of effective retention

2021 ◽  
Vol 189 ◽  
pp. 312-319
Author(s):  
Alaidine Ben Ayed ◽  
Ismaïl Biskri ◽  
Jean-Guy Meunier
Keyword(s):  
Text Summarization ◽  
Arabic Text ◽  
Arabic Text Summarization

scholarly journals Correction to: Arabic text summarization using deep learning approach

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Molham Al-Maleh ◽  
Said Desouki
Keyword(s):  
Deep Learning ◽  
Text Summarization ◽  
Learning Approach ◽  
Arabic Text ◽  
Arabic Text Summarization

An amendment to this paper has been published and can be accessed via the original article.

Unsupervised neural networks for automatic Arabic text summarization using document clustering and topic modeling

2021 ◽  
Vol 172 ◽  
pp. 114652
Author(s):  
Nabil Alami ◽  
Mohammed Meknassi ◽  
Noureddine En-nahnahi ◽  
Yassine El Adlouni ◽  
Ouafae Ammor
Keyword(s):  
Neural Networks ◽  
Topic Modeling ◽  
Document Clustering ◽  
Text Summarization ◽  
Arabic Text ◽  
Arabic Text Summarization ◽  
Unsupervised Neural Networks

Automatic Arabic Text Summarization Using Analogical Proportions

2020 ◽  
Vol 12 (5) ◽  
pp. 1043-1069
Author(s):  
Bilel Elayeb ◽  
Amina Chouigui ◽  
Myriam Bounhas ◽  
Oussama Ben Khiroun
Keyword(s):  
Text Summarization ◽  
Arabic Text ◽  
Arabic Text Summarization

scholarly journals Abstractive Arabic Text Summarization Based on Deep Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Y.M. Wazery ◽  
Marwa E. Saleh ◽  
Abdullah Alharbi ◽  
Abdelmgeid A. Ali
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Text Summarization ◽  
Arabic Text ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Arabic Text Summarization ◽  
Abstractive Summarization ◽  
Research Studies

Text summarization (TS) is considered one of the most difficult tasks in natural language processing (NLP). It is one of the most important challenges that stand against the modern computer system’s capabilities with all its new improvement. Many papers and research studies address this task in literature but are being carried out in extractive summarization, and few of them are being carried out in abstractive summarization, especially in the Arabic language due to its complexity. In this paper, an abstractive Arabic text summarization system is proposed, based on a sequence-to-sequence model. This model works through two components, encoder and decoder. Our aim is to develop the sequence-to-sequence model using several deep artificial neural networks to investigate which of them achieves the best performance. Different layers of Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) have been used to develop the encoder and the decoder. In addition, the global attention mechanism has been used because it provides better results than the local attention mechanism. Furthermore, AraBERT preprocess has been applied in the data preprocessing stage that helps the model to understand the Arabic words and achieves state-of-the-art results. Moreover, a comparison between the skip-gram and the continuous bag of words (CBOW) word2Vec word embedding models has been made. We have built these models using the Keras library and run-on Google Colab Jupiter notebook to run seamlessly. Finally, the proposed system is evaluated through ROUGE-1, ROUGE-2, ROUGE-L, and BLEU evaluation metrics. The experimental results show that three layers of BiLSTM hidden states at the encoder achieve the best performance. In addition, our proposed system outperforms the other latest research studies. Also, the results show that abstractive summarization models that use the skip-gram word2Vec model outperform the models that use the CBOW word2Vec model.

Sign in / Sign up

Export Citation Format

Share Document