This translates to low edge weights in the underlying similarity graph structure (between mentions), thus forming favorable candidates in the initial phases of cluster splitting using minimum edgecut based graph partitioning.
cROcS inherently incorporates early partition (during the clustering phase) of such possibly singleton mention clusters from the "main data", thereby helping in de-noising and efficiency.