Survey on Multi-Document Summarizer

Natural language processing provides Text Summarization which is the most popular application for information compression. Text summarization is a process of producing a summary by reducing the size of original document and pertaining important information of original document. There is arising a need to provide high quality summary in less time because in present time, the growth of data increases tremendously on World Wide Web or on user's desktops so Multi-Document summarization is the best tool for making summary in less time. This paper presents a survey of existing techniques with the novelties highlighting the need of intelligent Multi-Document summarizer. Natural language processing (NLP) is a field of computer science, artificial intelligence and machine learning with the interactions between computers and human language. The use of World Wide Web and many sources like Google, Yahoo! surfing also increases due to this the problem of overloading information also increases. There is huge amount of data available in structured and unstructured form and it is difficult to read all data or information. It is a need to get information within less time. Hence we need a system that automatically retrieves and summarize the documents as per user need in time limit. Document Summarizer is one of the feasible solutions to this problem. Summarizer is a tool which serves a useful and efficient way of getting information. Summarizer is a process to extract the important content from the documents. In general, the summaries are defined in two ways. They are Single Document Summarization and Multiple Document Summarization. The summary which is extracted and created from single document is called as Single Document Summarization whereas Multiple Document Summarization is an automatic process for the extraction and creation of information from multiple text documents. The main aim of summarization is to create summary which provides minimum redundancy, maximum relevancy and coreferent object of same topic of summary. In simple words, summary should cover all the major aspects of original document without irrelevancy while maintaining association between the sentences of summary. So, Extractive summarization and Abstractive summarization approach is used. Extractive summarization works by selecting existing words, phrases or number of sentences from the original text to form summary. It picks the most relevant sentences or keywords from the documents while it also maintains the low redundancy in the summary. Abstractive summarization method which generates a summary that is closer to what a human might create. Basically this type of summary might contain words not explicitly present in the original document format. It provides abstraction of original document form in fewer words. This survey covers Cluster Based approach, LDA Based approach and Ranking Based approach. The main aim of Multi-document summarization has been also elaborated. The remaining paper is presented as follows. Section II describes related work in the field of multi document summarization using Cluster Based approach, LDA Based approach and Ranking Based approach, Section III presents final conclusion.