Research Article Open Access

Automatic Multi Document Summarization Approaches

Yogan Jaya Kumar and Naomie Salim


Problem statement: Text summarization can be of different nature ranging from indicative summary that identifies the topics of the document to informative summary which is meant to represent the concise description of the original document, providing an idea of what the whole content of document is all about. Approach: Single document summary seems to capture both the information well but it has not been the case for multi document summary where the overall comprehensive quality in presenting informative summary often lacks. It is found that most of the existing methods tend to focus on sentence scoring and less consideration is given to the contextual information content in multiple documents. Results: In this study, some survey on multi document summarization approaches has been presented. We will direct our focus notably on four well known approaches to multi document summarization namely the feature based method, cluster based method, graph based method and knowledge based method. The general ideas behind these methods have been described. Conclusion: Besides the general idea and concept, we discuss the benefits and limitations concerning these methods. With the aim of enhancing multi document summarization, specifically news documents, a novel type of approach is outlined to be developed in the future, taking into account the generic components of a news story in order to generate a better summary.

Journal of Computer Science
Volume 8 No. 1, 2012, 133-140


Submitted On: 10 August 2011 Published On: 1 November 2011

How to Cite: Kumar, Y. J. & Salim, N. (2012). Automatic Multi Document Summarization Approaches. Journal of Computer Science, 8(1), 133-140.

  • 38 Citations



  • Multi document summarization
  • extractive summarization
  • news documents
  • summarization approaches
  • ontology learning
  • generic components
  • novel text summarization approaches