Domain Representative Keywords Selection: A Probabilistic Approach

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 $\mathit{\text{two-component}}\mathit{\text{mixture}}\mathit{\text{model}}$ concept to generate a distribution of candidate keywords. It provides more importance to the $\mathit{\text{distinctive}}$ keywords of the target domain than common keywords contrasting with the context domain. To support the $\mathit{\text{representativeness}}$ of the selected keywords towards the target domain, we introduce an $\mathit{\text{optimization}}\mathit{\text{algorithm}}$ 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.