Convolutional recurrent neural networks for text lecture summarization
Joint Authors
Abdulsahib, Muna Ghazi
Abd al-Munim, Mathil Imad al-Din
Source
Iraqi Journal of Computer, Communications and Control Engineering
Issue
Vol. 22, Issue 2 (30 Jun. 2022), pp.27-39, 13 p.
Publisher
Publication Date
2022-06-30
Country of Publication
Iraq
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Abstract EN
Text summarization can be utilized for variety type of purposes; one of them for summary lecture file.
A long document expended long time and large capacity.
Since it may contain duplicated information, more over, irrelevant details that take long period to access relevant information.
Summarization is a technique which provides the primary points of the whole document, and in the same time it will indicates the majority of the information in a small amount of time.
For this reason it can save user time, decrease storage, and increase transfer speed to transmit through the internet.
The summarization process will eliminate duplicated data, unimportant information, and also replace complex expression with simpler expression.
The proposed method is using convolutional recurrent neural network deep model as a method for abstractive text summarization of lecture file that will be great helpful to students to address lecture notes.
This method proposes a novel encoder-decoder deep model including two deep model networks which are convolutional and recurrent.
The encoder part which consists of two convolutional layers followed by three recurrent layers of type bidirectional long short term memory.
The decoder part which consists of one recurrent layer of type long short term memory.
And also using attention mechanism layer.
The proposed method training using standard CNN/Daily Mail dataset that achieved 92.90% accuracy.
American Psychological Association (APA)
Abdulsahib, Muna Ghazi& Abd al-Munim, Mathil Imad al-Din. 2022. Convolutional recurrent neural networks for text lecture summarization. Iraqi Journal of Computer, Communications and Control Engineering،Vol. 22, no. 2, pp.27-39.
https://search.emarefa.net/detail/BIM-1492878
Modern Language Association (MLA)
Abdulsahib, Muna Ghazi& Abd al-Munim, Mathil Imad al-Din. Convolutional recurrent neural networks for text lecture summarization. Iraqi Journal of Computer, Communications and Control Engineering Vol. 22, no. 2 (Jun. 2022), pp.27-39.
https://search.emarefa.net/detail/BIM-1492878
American Medical Association (AMA)
Abdulsahib, Muna Ghazi& Abd al-Munim, Mathil Imad al-Din. Convolutional recurrent neural networks for text lecture summarization. Iraqi Journal of Computer, Communications and Control Engineering. 2022. Vol. 22, no. 2, pp.27-39.
https://search.emarefa.net/detail/BIM-1492878
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 38-39
Record ID
BIM-1492878