Enhancing Text Summarization with Deep Learning: A Seq2Seq Model Approach Using Attention Mechanisms and Stacked-LSTM Networks

The goal of this research is to use deep learning techniques to create a Seq2Seq text summarization model, with Python as the implementation language. The degree to which the algorithm produces summaries that faithfully capture important elements of the source texts is a measure of its effectiveness. The size and caliber of the training dataset, the design of the Seq2Seq model, and hyperparameter modifications all have a big impact on performance. To increase summarization accuracy, we investigate improvements such as attention methods and different model designs through iterative testing and improvement. By collecting contextual information from both input directions and improving the output summary’s richness, stacked LSTM networks are suggested as a way to greatly boost performance. Furthermore, we propose to use the beam search decoding strategy instead of the greedy approach to achieve more coherent results. The performance of the model is objectively assessed using the BLEU score, which also serves as a benchmark for summary quality. Furthermore, we discuss how to address common summarization task problems by combining coverage and pointer-generator networks. All things considered, our findings show how deep learning may be used to automatically generate brief and instructive text summaries, highlighting the need to continue refining the model and expanding the dataset to get the best results. The precision of this machine learning model is 70-85%.