Review for NeurIPS paper: Fairness in Streaming Submodular Maximization: Algorithms and Hardness

Additional Feedback: As I mentioned before, I would encourage authors to change the initial framing of the paper, which promises to "giv[e] affirmative answers" to the question of whether "it is possible to create fair summaries for massive datasets". I think this part of the abstract might be better served describing the fairness constraint, so that the reader better understands this aspect. There also seems to be some odd formatting with bold and numbered text in the first paragraph on Section 6. 2. On Line 22, the sentence "submodularity is a natural way to capture the diminishing returns property of set functions, which holds for a variety of machine learning problems" is slightly awkward and misleading as written. It appears as if all set functions have an inherent diminishing returns property and that submodularity is capturing this. Of course, fixing this is just a matter of rephrasing.