A Trainable Document Summarizer Using Bayesian Classifier Approach

This paper presents an investigation into machine learning approach for document summarization. A major challenge related to document summarization is selection of features and learning patterns of these features which determines what information in source should be included in the summary. Instead of selecting and combining these features in ad hoc manner which would require readjustment for each new genre, natural choice is to use machine learning techniques. This is the basis for trainable machine learning approach to summarization. We briefly discuss design, implementation and performance of Bayesian classifier approach for document summarization.