EStep: A novel method for semantic text summarization with web-based big data

Abstract

Text summarization plays an important role in analysis of large set of data. It can be use in online text analysis and knowledge representation. Semantic text summarization plays a vital role to handle big data as data is in very large size, dynamic in nature and heterogeneity. In this paper I have proposed a novel model of knowledge-based semantic analysis for text summarization of web-based dynamic text data with help of FP-tree (Frequent Pattern tree). This model is free from ontology to find out semantic representation. The model consists of two phases. In the first phase benchmark web text data in terrorism domain is collected for construction of domain knowledge representation using FP-tree. Preprocessing is performed to reduce size and handle synonyms. In the second phase, Online articles/news are collected from different sources. Then using the domain knowledge representation, the summary of the web based large text data is extracted.