Text Summarization is the process of creating a condensed form of text document which maintains significant information and general meaning of source text. Automatic text summarization becomes an important way of finding relevant information, precisely in large text, in a short span of time. In this paper, the proposed method uses sentence ranking of a topic-specific document to generate automatic summary. The method is based on the concept of extractive summary, in which the summary of a document is obtained by scoring, ranking and selecting the highest ranked sentences of the document. Initially, the text is pre-processed by tagging the document and selecting adjectives, nouns and verbs, and then the text is analysed and sentences most similar to all is ranked and selected for generation of summary. Experiments on these methods were conducted to compare the results on sentence ranking. The algorithm proposed was tested on different documents and has given accuracy of about 80% when compared to summarization tools available online.

Text Summarization, data mining, extraction-based summarization, sentence ranking

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