  With increasing data, techniques for finding smaller, yet effective subsets
with specific characteristics become important. Motivated by this, we present
PRISM, a rich class of Parameterized Submodular Information Measures, that can
be used in applications where such targeted subsets are desired. We demonstrate
the utility of PRISM in two such applications. First, we apply PRISM to improve
a supervised model's performance at a given additional labeling cost by
targeted subset selection (PRISM-TSS) where a subset of unlabeled points
matching a target set are added to the training set. We show that PRISM-TSS
generalizes and is connected to several existing approaches to targeted data
subset selection. Second, we apply PRISM to a more nuanced targeted
summarization (PRISM-TSUM) where data (e.g., image collections, text or videos)
is summarized for quicker human consumption with additional user intent.
PRISM-TSUM handles multiple flavors of targeted summarization such as
query-focused, topic-irrelevant, privacy-preserving and update summarization in
a unified way. We show that PRISM-TSUM also generalizes and unifies several
existing past work on targeted summarization. Through extensive experiments on
image classification and image-collection summarization we empirically verify
the superiority of PRISM-TSS and PRISM-TSUM over the state-of-the-art.
