Improved Text Summarization of News Articles Using GA-HC and PSO-HC

Muhammad Mohsin,Hwan-Seung Yong,Jung-In Choi,Muhammad Haneef,Usman Tariq,Shazad Latif,Sefedine Kadry,Muhammad Attique Khan

doi:10.3390/app112210511

Abstract

Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarization is time consuming, and it is difficult to read and summarize a large amount of content. Automatic text summarization is the solution to deal with this problem. This study proposed two automatic text summarization models which are Genetic Algorithm with Hierarchical Clustering (GA-HC) and Particle Swarm Optimization with Hierarchical Clustering (PSO-HC). The proposed models use a word embedding model with Hierarchal Clustering Algorithm to group sentences conveying almost same meaning. Modified GA and adaptive PSO based sentence ranking models are proposed for text summary in news text documents. Simulations are conducted and compared with other understudied algorithms to evaluate the performance of proposed methodology. Simulations results validate the superior performance of the proposed methodology.

Highlights

The internet technology known as World Wide Web has seen a lot of advancements in last two decades
ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation
ROUGE evaluates the summaries generated by the machine learning model with the summaries created by humans

Summary

Introduction

The internet technology known as World Wide Web has seen a lot of advancements in last two decades. In current era internet is cheap and available all around the world. This gave rise to exponential growth of information [1]. Users usually do not read entire web pages or articles, instead users just scan the entire pages or articles just to retrieve few sentences or parts of those sentences to obtain the main crux of the whole information in that article or web page [2]. With such a huge amount of information, it is difficult for the user to identify an important part or parts of sentences which hold the main crux of entire article in less time and with great precision and accuracy [3]

Methods

Results

Conclusion