Convolutional Recurrent Neural Networks for Text Lecture Summarization | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 3, Volume 22, Issue 2, June 2022, Pages 27-39 PDF (1.06 M) | ||
| Document Type: Research Paper | ||
| DOI: https://doi.org/10.33103/uot.ijccce.22.2.3 | ||
| Authors | ||
| Muna Ghazi* 1; Matheel Abdulmunim2 | ||
| 1Department of computer sciences, University of Technology, Baghdad, Iraq. | ||
| 2Department of computer sciences, University of Technology, Baghdad, Iraq | ||
| Abstract | ||
| 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. | ||
| Keywords | ||
| Text Summarization; Deep learning; Convolutional Neural Network; Long Short Term Memory; Convolutional Recurrent Neural Network | ||
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