Abstract
Text summarization is a method which generates a shorter and a preciseform of one or more text documents. Automatic text summarization plays an essential role in finding information from large text corpus or an internet. What had actually started as a single document Text Summarization has now evolved and developed into generating multi-document summarization. There are a number of approaches to multi-document summarization such as Graph, Cluster, Term-Frequency, Latent Semantic Analysis (LSA) based etc. In this paper we have started with introduction of multi-document summarization and then have further discussed comparison and analysis of various approaches which comes under the multi-document summarization. The paper also contains details about the benefits and problems in the existing methods. This would especially be helpful for researchers working in this field of text data mining. By using this data, researchers can build new or mixed based approaches for multidocument summarization.
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