Role of classification in automated extractive text summarization with linear regression and summary refinement

Abstract

Text mining is a relatively new field emerging in many disciplines It is becoming more popular as technology advances and the need for efficient text analysis is required The aim of text mining itself is not to provide strict rules by analysing the full data set but is used to predict with some certainty while only analysing a small portion of the text The increasing availability of online information has necessitated intensive research in the area of automatic text summarization within the Natural Language Processing Community Extensive use of internet is perhaps one of the main reasons why automatic text summarization draws substantial interest newlineText Summarization is the task of producing a summary as a text that is produced from one or more texts that convey important information in the original text and that is no longer than half of the original text and usually significantly less than that Depending on its form a summary can be classified as extractive or abstractive the former being an actual representation of paragraphs sentences or phrases from the original document and the latter being a concise summary of the central subject matter of a document Single document extractive text summarization is focused in this thesis and unique features that will help in selecting summary sentences are also identified and extracted newline newline