Using data merging techniques for generating multidocument summarizations

In this paper, we examine how we can use data merging techniques to summarize a set of coreferent documents that has been clustered while using soft computing techniques. The main focus of this paper lies on the f(beta)-optimal merge function (a function newly introduced here), which that uses the weighted harmonic mean to find a balance between precision and recall. The global precision and recall measures mentioned are defined by means of a triangular norm receiving local precision and recall values as an input, in order to generate a multiset of key concepts that we can use to generate summarizations. The f(beta)-optimal merge function is compared with a distance-based merge function and several pointwise merge functions from both a theoretical and an experimental point of view. It will be shown that the f(beta)-optimal merge function has quite a few advantages over the others, especially if one looks at the practical usage in the context of data merging and summarizing multiple documents concerning the same topic.

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