Improving clustering quality by automatic text summarization

Abstract

Automatic text summarization is the process of reducing the size of a text document, to create a summary that retains the most important points of the original document. It can thus be applied to summarize the original document by decreasing the importance or removing part of the content. The contribution of this paper in this field is twofold. First we show that text summarization can improve the performance of classical text clustering algorithms, in particular by reducing noise coming from long documents that can negatively affect clustering results. Moreover, the clustering quality can be used to quantitatively evaluate different summarization methods. In this regards, we propose a new graph-based summarization technique for keyphrase extraction, and use the Classic4 and BBC NEWS datasets to evaluate the improvement in clustering quality obtained using text summarization.