The popularity of k -mer-based methods has recently led to the development of compact k -mer-set representations, such as simplitigs/Spectrum-Preserving String Sets (SPSS), matchtigs, and eulertigs. These aim to represent k -mer sets via strings that contain individual k -mers as substrings more efficiently than the traditional unitigs. Here, we demonstrate that all such representations can be viewed as superstrings of input k -mers, and as such can be generalized into a unified framework that we call the masked superstring of k -mers. We study the complexity of masked superstring computation and prove NP-hardness for both k -mer superstrings and their masks. We then design local and global greedy heuristics for efficient computation of masked superstrings, implement them in a program called KmerCamel, and evaluate their performance using selected genomes and pan-genomes. Overall, masked superstrings unify the theory and practice of textual k -mer set representations and provide a useful framework for optimizing representations for specific bioinformatics applications.