Survey on Extractive Text Summarization Approaches

Due to increasing use of internet and online technologies or online data, there is vast increase in the electronic documents. When a data is being retrieved from such a huge collection of electronic documents, hundreds and thousands of documents are retrieved. Hence, for user, it is not possible to read all the retrieved documents. Also, these documents contain redundant information. In such situation summarization proves to be very useful that summarizes the retrieved documents. This has lead to intensive research in the area of automatic text summarization. It is widely used in other fields like natural language processing and machine learning. Text summarization is the process of generating summary of one or more documents which conveys information present in the documents and is usually less than the original documents. This research paper provides an overview of the extractive techniques for text summarization. Two main approaches focused are clustering techniques and machine learning technique (SVM). Keywords— abstractive summarization, clustering, extractive summarization, pre-processing, SVM