title: Proper Aggregate Measure of Cosine Similarity? (based on text similarity)
body: <p>I am trying to find a proper aggregate measure for my users.</p>
  <p>The user can input <code>m</code> number of keywords, where each keyword computes cosine similarity, encoded by a pre-trained model, against <code>n</code> number of tokens, resulting a <code>m x n</code> matrix.</p>
  <p>Imagine there are 3 sets of tokens, then I would have three matrices, <code>m x n1</code>, <code>m x n2</code>, and <code>m x n3</code>, each representing the semantic text similarity to each set of tokens.</p>
  <p>I know there are multiple aggregate measures, and this type of problem is sensitive to the methods applied. What is the proper way to aggregate each matrix to a scaler, so that I could find the most similar sets of tokens?</p>