Transforming Text Summarization with Deep Neural Networks

Abstract

The advent of deep neural networks has revolutionized the field of text summarization, offering unprecedented capabilities for extracting meaningful summaries from large text corpora. This paper explores the transformation of text summarization processes through the application of advanced deep learning algorithms. We investigate various neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more specifically, long short-term memory (LSTM) networks, to assess their efficacy in generating coherent and contextually relevant summaries. By analyzing the performance metrics across different datasets, we demonstrate the superiority of deep learning methods over traditional approaches. Our findings suggest that deep neural networks not only enhance the quality of text summaries but also provide robust mechanisms for handling diverse and complex linguistic structures. This study lays the groundwork for further research into optimizing neural network parameters and architectures to achieve even more effective summarization outcomes.