Publication

Abstract : The Text Summarization is one of the problem under Natural Language Processing. This system which gives a single summarized document from multiple related documents. The summarizer provides an accurate result to the input query in the form of a precise text document by analyzing the text from various text document clusters. There are two methodologies- Clustering and Support Vector Machine (SVM) are used to solve this NLP problem. The present text summarizer system uses either SVM or Clustering technique. In this work we propose a Hybrid approach to serve our purpose by cascading both techniques to get an improved summary of data on related documents. We pre process the documents to get tokens obtained after stemming and stop word removal. The hybrid approach helps in summarizing the text documents efficiently by avoiding redundancy among the words in the document and ensures highest relevance to the input query. The guiding factors of our results are the ratio of input to output sentences after summarization. © Research India Publications.