  We propose a probabilistic approach to select a subset of a \textit{target
domain representative keywords} from a candidate set, contrasting with a
context domain. Such a task is crucial for many downstream tasks in natural
language processing. To contrast the target domain and the context domain, we
adapt the \textit{two-component mixture model} concept to generate a
distribution of candidate keywords. It provides more importance to the
\textit{distinctive} keywords of the target domain than common keywords
contrasting with the context domain. To support the \textit{representativeness}
of the selected keywords towards the target domain, we introduce an
\textit{optimization algorithm} for selecting the subset from the generated
candidate distribution. We have shown that the optimization algorithm can be
efficiently implemented with a near-optimal approximation guarantee. Finally,
extensive experiments on multiple domains demonstrate the superiority of our
approach over other baselines for the tasks of keyword summary generation and
trending keywords selection.
