An Improved Approach to Extract Document Summaries Based on Popularity

With the rapid growth of the Internet, most of the textual data in the form of newspapers, magazines and journals tend to be available on-line. Summarizing these texts can aid the users access the information content at a faster pace. However, doing this task manually is expensive and time-consuming. Automatic text summarization is a solution for dealing with this problem. For a given text, a text summarization algorithm selects a few salient sentences based on certain features. In the literature, weight-based, foci-based, and machine learning approaches have been proposed. In this paper, we propose a popularity-based approach for text summarization. A popularity of the sentence is determined based on the number of other sentences similar to it. Using the notion of popularity, it is possible to extract potential sentences for summarization that could not be extracted by the existing approaches. The experimental results show that by applying both popularity and weight-based criteria it is possible to extract effective summaries.