A novel Ensemble Learning Model for Text Summarization with Improved Multilayer Extreme Learning Machine Auto Encoder (MLELM-AE) Using Machine Learning Algorithms

Abstract

The gigantic amount of electronic data gathered and analyzed has contributed to useful information sources that human beings need to manage easily. To make important decisions very quickly, the Automatic Text Summarization (ATS) method enables users to get relevance and expertise in a very small amount of time. ATS systems are notably extractive and abstractive, or by combining these two approaches, it is also used as a hybrid. The extractive technique involves extracting the most important sentences from the input document(s), then assembling these sentences to produce the summary. In the abstractive technique, a summary is produced by creating new sentences instead of picking sentences to convey the meaning of input text. A hybrid method intermingles of Abstractive and Extractive methods. In spite of many suggested techniques, the created summaries still don’t convey the actual meaning of text as compared to the man-made summaries. In this paper, the authors reviewed different techniques of text summarization and identified that the present models are computationally expensive and low training speed. To address this problem authors proposed an improved Multilayer Extreme Learning Machine Auto Encoder (MLELM-AE) and an Ensemble Learning Model for Text Summarization Using machine learning algorithms to enhance the quality of the summary. The proposed model is an ensemble of four models, i.e., Sentence2BERT (S2B), Auto Encoder (AE), Variational Auto Encoder (VAE), and Improved MLELM-AE. The performance of this in respect of execution time is 50015(ms) and ROUGH1 score of ensemble model is 0.865195. Thus, the results demonstrated that the recommended model is much better for text summarization as compared to existing models.