This paper aims to study the importance of considering the documentation side of GitHub repositories when assessing the similarity between two or more applications. Readme and Wiki files, along with Comments from the source files, are the dimensions proposed to be analyzed through our methodology and experiments. We propose a pipeline that first extracts text fragments from these dimensions and then applies Natural Language Processing techniques to further prepare our data for evaluation. To gather a similarity score, we first vectorize our processed data with TF-IDF and then use cosine distance to obtain the score. Combinations of the three dimensions, ranging from using only one dimension to using all of them, are considered throughout our study. Moreover, additional information has been extracted from the plain text, such as referenced URLs and License usage, the similarity of which was calculated using Jaccard distance. Two experiments were performed. The first one aims to observe the behavioral tendencies of our methodology applied to a small dataset, while the second one aims to validate our results. By evaluating them, we found sufficient data that supported our presented conclusion: documentation represents a valuable asset in gathering a pool of similar applications.