RNN-based Text Summarization for Communication Cost Reduction: Toward a Semantic Communication

Text summarization has become a subject of great importance because of the continuous global growth of internet technology. The frequent occurrence of the same textual information creates an overhead in the communication network. Extracting the summary from a large document is quite challenging for any human being. Text summarization can play a vital role in this regard. To be precise, text summarization is the process of automatically producing and condensing the content of a given document into a more manageable form with a coherent message. Even though it only contains a few sentences, this concise explanation would nonetheless effectively convey the key idea. This illustrates how text summarization retains the essential information while substantially lowering the quantity of information that must be communicated. In this research, we propose a system model architecture where a central base station sends the summarized text to the users on their edge devices. We also demonstrate how the communication cost is reduced with each transmission of the condensed text. To ensure the best possible summary, we choose the long short-term memory recurrent neural network (LSTM-RNN) since RNN-based deep learning models have achieved great success in text analysis over the past few years. Additionally, the experimental results also show that our proposed model, in combination with LSTM-RNN reduces communication costs by an average of 85%.