This paper discusses the problem of managing rules for page layout analysis and information extraction. We have been developing a system to extract information from academic papers that exploits both page layout and textual information. For this purpose, a conditional random field (CRF) analyzer is designed according to the layout of the object pages. Because various layouts are used in academic papers, we must prepare a set of rules for each type of layout to achieve high extraction accuracy. As the number of papers in a system grows, rule management becomes a big problem. For example, when should we make a new set of rules, and how can we acquire them efficiently while receiving new articles? This paper examines two scores to measure the fitness of rules and the applicability of rules learned for another type of layout. We evaluate the scores for bibliographic information extraction from title pages of academic papers and show that they are effective for measuring the fitness. We also examine the sampling of training data when learning a new set of rules.