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.

该研究提出了一种概率方法，通过对目标领域和上下文领域进行对比，采用包含候选关键词的双分量混合模型来选择一组目标领域代表性关键词，然后使用优化算法从生成的候选分布中选出代表性最好的关键词，研究表明该方法优于其他基线方法，尤其适用于关键词摘要生成和趋势关键词选择。