Analysis and Transformation of Textual Energy Distribution

In this paper we revisit the Textual Energy model. We deal with the two major disadvantages of the Textual Energy: the asymmetry of the distribution and the unbounded ness of the maximum value. Although this model has been successfully used in several NLP tasks like summarization, clustering and sentence compression, no correction of these problems has been proposed until now. Concerning the maximum value, we analyze the computation of Textual Energy matrix and we conclude that energy values are dominated by the lexical richness in quadratic growth of the vocabulary size. Using the Box-Cox transformation, we show empirical evidence that a log transformation could correct both problems.