Text Summarization of News in Tamil Epaper Using Machine Learning

Conference: The Paris Conference on Education (PCE2022)
Title: Text Summarization of News in Tamil Epaper Using Machine Learning
Stream: Language Development & Literacy
Presentation Type: Virtual Presentation
Vellingiriraj Erode Krishnasamy, Nandha Engineering College, India
Balamurugan M, Christ University, Bengaluru, India
Nalini S, PSG College of Technology, India
Saraswathi K, Kongu Engineering College, India
Renukadevi NT, Kongu Engineering College, India


Automatic text summarization is the process of reducing the size of original content to reduced computational burden of handling original large volume of data. There are many research works has been introduced earlier for the automatic text summarization. In the existing work Machine Learning based Automatic Text Summarization (MLATS) is introduced for the automatic summarization outcome. However, this research work doesn’t focus on semantic properties and interrelationship between different contents. This is resolved in the proposed work by introducing the method namely Ranking based Hierarchical Clustering Summarization Technique (RHCST). Initially, hierarchical clustering method is introduced to cluster the sentences which provide the similar meaning. And then optimal summarization is performed using Modified GA and Adaptive PSO method. Here accurate text summarization is ensured by adapting the ranking method along with modified GA and Adaptive PSO method. The overall implementation of the research work is done in the matlab simulation environment from which it is proved that the proposed method attains better outcome than the existing methodology. In this paper, two variations of programmed text summarization model are introduced. The proposed approach utilized distributional semantics of the words present in the sentences of the message and involved hierarchical clustering procedure for gathering comparative sentences. GAHC applied GA for streamlining the aftereffects of extricated features while RPSO involved PSO for upgrading the consequences of separated features. From this research, it is proved that the proposed RHCST shows 98.2% accuracy with 1.2% increased than MLATS.

Virtual Presentation

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