Automatic text summarization with statistical and linguistic features using successive thresholds

Text summarization is an emerging technique for finding out the summary of the text document. Text summarization is nothing but summarizing the content of given text document. Text summarization has got so uses such as Due to the massive amount of information getting increased on internet; it is difficult for the user to go through all the information available on web. Summarization techniques need to be used to reduce the user's time in reading the whole information available on web. In this paper, we propose an automatic text summarization technique using both linguistic and statistical features using successive threshold for finding the summary i.e important sentences from the given input text document. Here the sentences are selected for summary based on the weight of the sentence. The weight of the sentences is calculated based on the statistical and linguistic features. Our approach assigns scores to the sentences by weighting the features like term frequency, word occurrences, and noun weight, phrases etc. In our approach, the number of sentences present in our summary would be equal to the number of paragraphs present in a text document, which can be achieved by using our successive threshold approach.