OVERVIEW OF TEXT SUMMARIZATION EXTRACTIVE TECHNIQUES (International Journal of Engineering and Computer Science, 2017)
Text summarization plays an important role in the area of natural language processing and text mining. As the information resources in both online and offline are increasing exponentially, the major challenge is to find relevant information from large amount of data in short time. Text summarization aims to create a compressed summary while retaining the main characteristics of the original set of documents. Many approaches use statistics and machine learning techniques to extract sentences from documents. An extractive summarization method consists of selecting important Sentences, paragraphs etc. from the original document and concatenating them into shorter form. The importance of sentences is decided based on statistical and linguistic features of sentences. An abstractive summarization method consists of understanding the original text and re-telling it in fewer words. It uses linguistic methods to examine and interpret the text and then to find the new concepts and expressions to best describe it by generating a new shorter text that conveys the most important information from the original text document.  This paper presents a survey of extractive text summarization techniques. We explore different types of summarization, evaluation strategies and metrics, features, extractive summarization techniques, approaches and problems in text summarization.