Concept Based Extractive Text Summarization Using Graph Modelling and Weighted Iterative Ranking
Text summarization is a process of reducing the whole text document into a summary by retaining the most important information and to present it to the end user. Wikipedia which is a human generated knowledge base is used to identify the key sentences using weighted iterative ranking algorithm which is the variation of HITS algorithm. The pre processed input document is used in the construction of bipartite graph which maps the input sentences to the Wikipedia concepts. The bipartite graph captures the nested level of relationship between the sentences and concepts to ensure the highest level of efficiency in the extractive output summary. Weighted iterative ranking algorithm is used to retrieve top ranked sentences. The system produce summaries with good coverage, high coherency and low redundancy. The system can be deployed to summarize news articles, producing abstracts from documents, summarize web pages. The new article summarization would be helpful to mobile users.