Tailoring text for automatic layouting of newspaper pages

We consider layouting news articles on a page as a cutting and packing problem with output maximization. We propose to tailor news articles by employing automatic summarization to find new solutions for the packing problem. Tailoring text items allows us to use an efficient isin-approximate greedy layouting algorithm, which scales well for larger data sets, to explore the search space. We also propose a function for adjusting the value of summarized articles. Our results show that the overall solution value as well as the individual quality of articles are notably improved, with the solution value in some cases even exceeding the optimum score achievable by an exhaustive search without summarization.