Arabic text summarization using deep learning approach

Natural language processing has witnessed remarkable progress with the advent of deep learning techniques. Text summarization, along other tasks like text translation and sentiment analysis, used deep neural network models to enhance results. The new methods of text summarization are subject to a se...

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Vydáno v:Journal of big data Ročník 7; číslo 1; s. 1 - 17
Hlavní autoři: Al-Maleh, Molham, Desouki, Said
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 11.12.2020
Springer Nature B.V
SpringerOpen
Témata:
Arabic language
Artificial neural networks
Automatic summarization
Big Data
Coders
Communications Engineering
Computational Science and Engineering
Computer Science
Data
Data mining
Data Mining and Knowledge Discovery
Database Management
Datasets
Deep learning
Employment
Information Storage and Retrieval
Language acquisition
Learning
Machine learning
Mathematical Applications in Computer Science
Natural language processing
Networks
Neural networks
Sentiment analysis
Sequence-to-sequence framework
Social networks
Text summarization
Deep learning
Natural language processing
Big data
Text summarization
Sequence-to-sequence framework
ISSN:2196-1115, 2196-1115
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Shrnutí:Natural language processing has witnessed remarkable progress with the advent of deep learning techniques. Text summarization, along other tasks like text translation and sentiment analysis, used deep neural network models to enhance results. The new methods of text summarization are subject to a sequence-to-sequence framework of encoder–decoder model, which is composed of neural networks trained jointly on both input and output. Deep neural networks take advantage of big datasets to improve their results. These networks are supported by the attention mechanism, which can deal with long texts more efficiently by identifying focus points in the text. They are also supported by the copy mechanism that allows the model to copy words from the source to the summary directly. In this research, we are re-implementing the basic summarization model that applies the sequence-to-sequence framework on the Arabic language, which has not witnessed the employment of this model in the text summarization before. Initially, we build an Arabic data set of summarized article headlines. This data set consists of approximately 300 thousand entries, each consisting of an article introduction and the headline corresponding to this introduction. We then apply baseline summarization models to the previous data set and compare the results using the ROUGE scale.
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ISSN:2196-1115
2196-1115
DOI:10.1186/s40537-020-00386-7