We introduce a text representation method which is initially based on a variety of closeness relationships between pairs of words in text passages within the entire corpus. This representation is then used as the basis for our multi-level lightweight ontological representation method (TOR-FUSE). We believe choosing the correct granularity of representation is an important aspect of text classification. The multi-level model will allow data interpretation in a more conceptual space, rather than only containing scattered words occurring in texts. It aims to perform the extraction of the knowledge tailored for the classification task by automatic creation of a lightweight ontological hierarchy of representations. The data representation will train a tailored ensemble learner over a stack of representations at different conceptual granularities. The final result is a mapping and weighting of the targeted concept of the original learning task, over a stack of representations and granular conceptual elements in its different levels (hierarchical mapping instead of linear mapping over a vector). The entire algorithm is applied to three text classification tasks, and the performance is evaluated in comparison with well-known methods.