Abstract
Text summarization is a process of distilling the most important content from text documents. While human beings have proven to be extremely capable summarizers, computer based automatic abstracting and summarizing has proven to be extremely challenging tasks. In this paper we report our experience with applying extractive summarization techniques to process news articles, economic reports and nursing narratives. We present analysis of the effect of different summarization methods and parameters on the summarization results. We also compare the performance of the summarizers across the three different document genres. The learned lessons are discussed and the possibilities for applying the theory of Computing with Words in text summarization are elaborated.
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