Semantic Similarity Based Automatic Document Summarization Method
K. Srinivasa Rao1, D.S. R. Murthy2, Gangadhara Rao Kancherla3

1K Sriniva sa Rao*, Research Scholar, JNTUK Asst professor Dept of IT, RVR & JC College of engineering, Guntur, Andhra Pradesh, India.
2D S R Murthy, Professor, Dept. of CSE, Geethanjali College of Engineering and Technology. Hyderabad, Telangana, India.
3Gangadhara Rao Kancherla, Professor, Dept. of CSE, ANU, , Guntur, Andhra Pradesh, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2516-2522 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8566088619/2019©BEIESP | DOI: 10.35940/ijeat.F8566.088619
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Abstract: Document summarization is the process of generating the summary of the documents gathered from the web sources. It reduces the burden of web readers by reducing the necessity of reading the entire document contents by generating the short summary. In our previous research work this is performed by introducing the method namely Noun weight based Automated Multi-Document Summarization method (NW-AMDSM). However the previous research work doesn’t concentrate on the semantic similarity which might reduce the accuracy of the summarization outcome. This is resolved in the proposed research method by introducing the method namely Semantic Similarity based Automatic Document Summarization Method (SS-ADSM). In this research work, multi document grouping is done is based on semantic similarity computation, thus the document with similar contents can be grouped more accurately. Here the semantic similarity computation is performed with the help of word net analyzer. The document grouping is done by introducing the modified FCM clustering algorithm. Finally hybrid neuro fuzzy genetic algorithm is introduced to perform the automatic summarization. The numerical analysis of the proposed research method is conducted in the matlab simulation environment and compared with other research methods in terms various performance metrics. The simulation analysis proved proposed method tends to have better performance in terms of increased accuracy of document summarization outcome.
Keywords: Document summarization, semantic similarity, fuzzy classifier, genetic algorithm, similarity grouping.