Submodular sparsification generalizes the classical sparsification problems of graphs and matrices to summations of submodular functions. Given the summation $F(S):=f_1(S)+\cdots+f_m(S)$ of $m$ submodular functions $f_1,\ldots,f_m:\{0,1\}^n \to \mathbb{R}_{\ge 0}$. An size-$s$ sparsification of $F$ is a weight vector $w \in \mathbb{R}^m_{\ge 0}$ such that $w_1 f_1(S) + \cdots w_m f_m(S) \approx F(S)$ for every subset $S \subset [n]$. Motivated by the wide applications of submodular functions in data mining and economics, submodular sparsification has been studied in the last few years. For general submodular functions, Kenneth and Krauthgamer provided an efficient construction of size $O(n^3)$. Although several families of submodular functions admit sparsifiers of size $\tilde{O}(n)$, there is a lower bound $Ω(n^2)$ on the size of sparsifiers by Cohen et al. In this work, we study whether cardinality constraints, such as restricting $S$ to subsets of size at most $k$, could reduce the size of sparsifiers or not. Namely, if the guaranty is $w_1 f_1(S) + \cdots w_m f_m(S) \approx F(S)$ for every $S$ in $[n]$ of cardinality at most $k$, are there sparsifiers of size smaller than $o(n^2)$? Our main result shows an efficient construction of size-$O(n k^2 \log n)$ sparsifiers for summations of arbitrary submodular functions. This improves the $Ω(n^2)$ bound for the general setting. Then we consider the existence of size-$(k \log n)^{O(1)}$ sparsifiers under the constraint of cardinality at most $k$ and show several natural families do not admit such a small sparsifier. Technically, our algorithm applies the Lovász extension and Edmonds' greedy algorithm to extend Kenneth and Krauthgamer's approach. In particular, we provide an efficient algorithm to provide a tight estimate (up to a constant) of the sensitivity of each $f_i$ under cardinality constraints.

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