Hierarchical probabilistic model for news composition.

ABSTRACTWe present a method for the automated composition of personalized newspapers. Traditional newsprint composition is a laborious and expensive manual process. We develop a two level hierarchical page layout model that models aesthetic design choices using local (within article region) and global (page level) prior probability distributions. Given content to be composed, our model can infer the best way to divide a page into layout regions and simultaneously optimize content fit within these regions. We automate decisions on how to paginate articles, flow article text across pages, crop images, adjust whitespace etc. for the best overall newspaper compositions. We also show how content editing which is a very important task in the traditional news workflow can be incorporated in a semi-automated manner within our framework. Our model is a generalization of our prior work on probabilistic modeling of single-flow layouts to enable multiple article flows on a page, while still allowing one or more articles that may break on a page and continue on subsequent pages.