Automatic generation of paragraph templates using a Textual Energy approach:

Abstract

In this paper, we present the results of preliminary experiments using the Textual Energy measure to be used in Automatic Text Generation tasks (ATG). Textual Energy calculates the similarity among the sentences of a document, using intuitive ideas coming from Mechanical statistical like the associative memories and the energy of a system. Using this approach we intend to generate a set of selected sentences having a semantic and structural coherence. In our experiment, the number of selected sentences was manually determined. In particular, the experiments were performed using sets of 4 sentences. Then, the selected sentences can be employed for paragraph generation using Canned Text-like techniques. We have performed an important number of experiments, and we found interesting results that we present in this paper. These results allow us to conclude that it is possible to generate a set of sentences, as paragraphs-like, through methods, avoiding as much as possible undesirable phenomena, such hallucination, which have been recently found in ATG models based on Deep learning Neural Networks.