There has been a great deal of interest in the past few years on ranking of results of queries on structured databases, including work on probabilistic information retrieval, rank aggregation, and algorithms for merging of ordered lists. In many applications, for example sales of homes, used cars or electronic goods, data items have a very large number of attributes. When displaying a (ranked) list of items to users, only a few attributes can be shown. Traditionally, these are selected manually. We argue that automatic selection of attributes is required to deal with different requirements of different users. We formulate the problem as an optimization problem of choosing the most "useful" set of attributes, that is, the attributes that are most influential in the ranking of the items. We discuss different variants of our notion of attribute usefulness, and propose a hybrid Split-Pane approach that returns a composite of the top attributes of each variant. We conduct both a performance and a user study illustrating the benefits of our algorithms in terms of efficiency and quality of explanation.