Alignment between a technical paper and presentation sheets using a hidden Markov model

We have been studying the automatic generation of presentation sheets from a technical paper. Our approach consists of obtaining a set of rules for generating presentation sheets by applying machine learning techniques to many pairs of technical papers and their presentation sheets collected from the World Wide Web. As a first step, in this paper, we propose a method for aligning technical papers and presentation sheets. Our method is based on Jing´s method, which uses a hidden Markov model (HMM). Although this method is useful to align short sentences in newspaper articles, it is inapplicable to align sentences in a paper including charts and long sentences. Therefore, we analyse features of papers and sheets, such as information from text appearance, and propose an alignment method that combines the use of these features and Jing´s method. The evaluation shows that our alignment method performed effectively.