Data Representation for Deep Learning - Based Summarization Performance Using Python Results

Mohamed Yassin Abdelwahab Yassin; Yazeed Al Moaiad

doi:10.15379/ijmst.v11i1.3646

Author(s)

Mohamed Yassin Abdelwahab Yassin Faculty of Computer and Information Technology, Al-Madinah International University, Taman Desa Petaling, 57100 Kuala Lumpur, Malaysia

Yazeed Al Moaiad Faculty of Computer and Information Technology, Al-Madinah International University, Taman Desa Petaling, 57100 Kuala Lumpur, Malaysia

Abstract

A sequence-to-sequence model is used as the foundation for a suggested abstractive Arabic text summarizing system. Our goal is to create a sequence-to-sequence model by utilizing multiple deep artificial neural networks and determining which one performs the best. The encoder and decoder have been developed using several layers of recurrent neural networks, gated recurrent units, recursive neural networks, convolutional neural networks, long short-term memory, and bidirectional long short-term memory. We are re-implementing the fundamental summarization model in this study, which uses the sequence-to-sequence framework. Using a Google Colab Jupiter notebook that runs smoothly, we have constructed these models using the Keras library. The results further demonstrate that one of the key techniques that has led to breakthrough performance with deep neural networks is the use of Gensim for word embeddings over other text representations by abstractive summarization models, along with FastText, a library for efficient learning of word representations and sentence classification.

Publication Details

Page(s) 339-356

DOI 10.15379/ijmst.v11i1.3646

Published Journal: International Journal of Membrane Science and Technology, Volume: 11, Issue: 1, Year: 2024

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Keywords

natural language processing, Machine learning, Deep Learning Model, Arabic Text Summarization, Sequence-to-Sequence

References

Al-Saleh A.B.,M.E.B. Menai A.B. .2016 .Automatic Arabic text summarization: a survey. Artificial Intelligence Review, 45 : 203-234.

Qassem Al,L.M. Al .2017 .Automatic Arabic summarization: a survey of methodologies and systems. Procedia Computer Science, 117 : 10-18.

Sarkar K. .2009 .Using domain knowledge for text summarization in medical domain. International Journal of Recent Trends in Engineering, 1(1) : 200.

Azmi A.M.,N.I. Altmami A.M. .2018 .An abstractive Arabic text summarizer with user-controlled granularity. Information Processing & Management, 54(6) : 903-921.

Sunitha C.,Jaya A.,Ganesh A. .2016 .A study on abstractive summarization techniques in Indian languages. Procedia Computer Science, 87 : 25-31.

Bakator M.,Radosav D. .2018 .Deep learning and medical diagnosis: A review of literature. Multimodal Technologies and Interaction, 2(3) : 47.

Zhang S.,Wang B.,Liu B. .2018 .Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4) : e1253.

Liu H. .2017 .Adaptive fuzzy synchronization for a class of fractional-order neural networks. Chinese Physics B, 26(3) : 030504.

Al-Maleh S.,Desouki S. .2020 .Arabic text summarization using deep learning approach. Journal of Big Data, : 1-17.

Abbasi-ghalehtaki H.,Khotanlou M.,Esmaeilpour M. .2016 .Fuzzy evolutionary cellular learning automata model for text summarization. Swarm and Evolutionary Computation, 30 : 11-26.

Zou W.Y. .2013 .Bilingual word embeddings for phrase-based machine translation. in Proceedings of the 2013 conference on empirical methods in natural language processing, : .

Sanchez-Gomez J.M.,Vega-Rodríguez M.A.,Perez C.J. .2019 .Comparison of automatic methods for reducing the Pareto front to a single solution applied to multi-document text summarization. Knowledge-Based Systems, 174 : 123-136.

Verma P. and H.,Om MCRMR .2019 .Maximum coverage and relevancy with minimal redundancy based multi-document summarization. Expert Systems with Applications, 120 : 43-56.

Mallick C. . .Graph-based text summarization using modified TextRank, in Soft computing in data analytics. 2019, : 137-146.

Kanapala A,Pal S.,Pamula R. .2019 .Text summarization from legal documents: a survey. Artificial Intelligence Review, 51(3) : 371-402.

Abdelwahab Y.,Al Moaiad Z.B.A.,Bakar Z.B.A. .2023 .Arabic Text Summarization using Pre-Processing Methodologies and Techniques. AsiaPacific Journal of Information Technology & Multimedia, 12(1) : .

Zhang Y.,Wallace B. .2015 .A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820, : .

Li Y.,Yang T. .2018 .Word embedding for understanding natural language: a survey. Guide to big data applications. , : 83-104.

Joulin A. .2016 .Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759, : .