Linguistic summaries of databases provide quick insight in the stored data and are important facilities for understanding and grasping the meaning of large data collections. This is especially relevant in the context of big data. However, large data collections often suffer from incomplete data, which are in the case of relational databases modelled by so-called null-values. In this paper we propose a novel soft computing technique for measuring the quality of a linguistic summary in the case of missing information. More specifically we describe and illustrate how bipolar satisfaction degrees can be used to model both the validity of the summary and the hesitation about this validity that might be caused due to missing information. The extra information about the hesitation provides the users with a semantically richer description of the summarization results, which is important in view of a correct interpretation.