Automatic Summarization Tool For Text Documents

Abstract

The increasing availability of online information has necessitated intensive research in the area of automatic text summarization within the Natural Language Processing (NLP) community. Automatic text summarization is technique of compressing the original text into shorter form which will provide same meaning and information as provided by original text. The brief summary produced by summarization system allows readers to quickly and easily understand the content of original documents without having to read each individual document. The overall motive of text summarization is to convey the meaning of text by using less number of words and sentences. Summaries are of two types: Abstractive summaries and Extractive summaries. Extractive summaries involve extracting relevant sentences from the source text in proper order. The relevant sentences are extracted by applying statistical and language dependent features to the input text. On the other hand, abstractive text summaries are made by applying natural language understanding. Human beings usually make summaries in abstractive way. Moreover abstractive summaries can also involve the words or sentences which are not present in the input text. Automatic generation of abstractive summary is more difficult as compared to producing extractive text summary. This has some applications like summarizing the search-engine results, providing briefs of big documents that do not have an abstract etc..In this project, an auto-summarization tool is developed using statistical techniques. The designed algorithm works in three steps. In the first step the document which is required to be summarized is processed by eliminating the stop word and by applying the stemmers. In the second step term-frequent data is calculated from the document and