A neural-based text summarization system

Abstract

The number of electronic documents as a media of business and academic information has increased tremendously after the introduction of the World Wide Web. Ever since, instances where users being overloaded with too much electronic textual information are inevitable. The users may only be interested in shorter versions of text documents but are overloaded with lengthy texts. The objective of the study is to develop a text summarization system that incorporates learning ability by combining a statistical approach, keywords extraction, and neural network with unsupervised learning. The system is able to learn to classify sentences when well trained with sufficient text samples. Users with strong background in writing English summaries have subjectively evaluated the outputs of the text summarization system based on contents. With the average contents score of 83.03%, the system is regarded to have produced an effective summary with most of the important contents of the original text extracted without compromising the summary's readability.

A Survey of Automatic Text Summarization: Progress, Process and Challenges

“…It incorporates a two-layer neural network with backpropagation, which is trained using the RankNet algorithm. An old TextSum system architecture including text preparation, keyword extraction, and summary creation was proposed in [230]. The system pre-processes the source document using two methods: stop word removal and stemming.…”

Backstepping-ANFIS Hybrid Controller Design for Cooperative Quadrotors

“…On the other hand, FL treats with logic on a higher-level, utilizing linguistic data obtained from range experts (Gupta). However, fuzzy systems cannot adapt themselves in new surroundings and haven't the learning capability which characterized the ANN approach (Yong & Tuan, 2006). The incorporation of FL and ANN enable combining the decision making capabilities of FL with learning capabilities of ANN (Kumari & Sunita, 2013).…”

Single Document Text Summarization Using Clustering Approach Implementing for News Article

“…Yong et al [5] worked on developing an automatic text summarization system by combining both a statistical approach and a neural network. Mohamed Abdel Fattah & Fuji Ren [6] applied a model based on a genetic algorithm (GA) and mathematical regression (MR) in order to obtain a suitable combination of feature weights to summarize one hundred English articles.…”