A Neural-based Text Summarization System
Author(s)
S. P. Yong, A. I. Z. Abidin & Y. Y. Chen
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.
Keywords: keyword extraction, neural network, unsupervised learning.
1 Introduction
The proliferation of electronic documents as a media for business and academic
information in the World Wide Web has resulted in users being overloaded by
electronic texts. Though users can sort out the documents through various search
engines, the engines usually do a poor approximation. The engines only show the
initial lines of the document. Users who do not use keywords to search for
intended document effectively might come across a vast quantity of hyperlinks.
Text summarization is an emerging field at the intersection of several research
areas, including natural language processing, machine learning and information
retrieval. It is essential to be able to extract the gist of the electronic documents
by having a text summarization system to fully utilize these documents
Keywords
keyword extraction, neural network, unsupervised learning.
Related Book
Other papers in this volume
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