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Text Summarization is the process of generating a short summary for the document that contains the overall meaning. This paper explains the extractive technique of summarization which consists of selecting important sentences from the document and concatenating them into a short summary. This work presents a method for identifying some feature terms of sentences and calculates their ranks. The relevance measure of sentences is determined based on their ranks. It then uses a combination of Statistical and Linguistic methods to identify semantically important sentences for summary creations. Performance evaluation is done by comparing their summarization outputs with manual summaries generated by three independent human evaluators.

An automatic Text Summarization using feature terms for relevance measure