A Survey to Automatic Summarization Techniques

This paper presents a review for automatic text summarization techniques. Text Summarization is extraction of required information from the huge documents by ignoring the irrelevant details with the help of distinct approaches and provides the summary in compact way which reduces the effort and time of user. Due to the huge availability of data in all the fields the management and study of the information become difficult. Due to this the interest in the research of new automatic summarization techniques has been increased. Different approaches are used to determine summary. Plain text and multilingual text summarization play very important role in summary generation. This paper provides the review of all the existing text summarization techniques. Keywords-Summary;UNL,NLization, UNLization;IAN; EUGENE;Multilingual. INTRODUCTION Automatic Text summarization has become very important part of our life due to huge volume of data that required to be compressed for people so, that it becomes possible to go through essential contents in short period of time. The research on the automatic text summarization has been started from 1950’s.After a gap of decade’s progress in the field of language processing was done due to the growing volume of online text. So, the large amount of electronic documents which are available in Internet has stimulated the development of excellent information retrieval systems. For example, Google always shows some part of the text corresponding to the query of the user. The user has to decide whether the document is interested or not only on the basis of extracted text. The user has to browse all the documents until a right document come. The solution is a use of automatic text summarizer which displays only the important and essential information about the document. Hence, it becomes very easy for the user to choose the right document. The demand of the text summarization has observed in various domains like education, medical, government offices, research, business etc. So, the development of automatic summarization systems has importance due to research point of view. There are different approaches used for text summarization on the basis of single document, multi document summarization APPLICATIONS OF TEXT SUMMRIES Text Summarization is used is in medical field, in multimedia news summarization, in producing intelligent reports, in text for hand held devices, in text-to-Speech for blind people, in education and in summarizing meetings [1]. Many other scenarios use text summarization. For example, an information retrieval system uses automatic summarization to produce the list of retrievals. Now a day’s summary of the email messages and news articles is sent to mobile devices as Short Message Service (SMS). Search engines also use summary mechanisms. The summary of the web pages is shown on the screen as a result of particular search. SUMMARIZATION TECHNIQUES a) Single Document Summarization In single document summarization only one document is provided for summary generation. It is a simple and earliest approach for summarization. Extractive and abstractive both summaries methods can be applied on single document summarization. b) Multi Document Summarization Multi document summarization is also very important part of summarization. More than one information sources are provided for summary generation. Many web based clustering systems like news were inspired from multi document summary. But task of multi document technique is more difficult and complex than single document techniques. The real aim is not only to remove redundancy International Journal of Engineering Research and General Science Volume 3, Issue 5, September-October, 2015 ISSN 2091-273