Newspaper pages feature a reasonable and well-organized news layout,allowing readers to access the most valuable information and gain pleasure in a short span.However,for typesetters,generating this type of newspaper lay-out is time-consuming.A method for automatically generating digital newspaper layouts is proposed by combining the Bayesian network inference and constrained programming.An inference model for the structure and attribute of digital newspaper layout is established using historical layout data and expertise to endow newly generated layout with a histor-ically specific style.Then,a mixed integer constraint programming model is proposed to compute the layout using the inference results.This approach aims to markedly reduce the solution space of the model and improve the layout quality.In addition,the inference model provides available candidate structures and generates varying results.The programming model also ensures that the layout has excellent alignment performance.A dataset with Chinese newspaper layouts is es-tablished to train and validate the model;it includes detailed news attribute label data.Research results exhibit the ef-fectiveness of the automated layout generation method.
维普
万方数据