  News articles typically mention numerous entities, a large fraction of which
are tangential to the story. Detecting the salience of entities in articles is
thus important to applications such as news search, analysis and summarization.
In this work, we explore new approaches for efficient and effective salient
entity detection by fine-tuning pretrained transformer models with
classification heads that use entity tags or contextualized entity
representations directly. Experiments show that these straightforward
techniques dramatically outperform prior work across datasets with varying
sizes and salience definitions. We also study knowledge distillation techniques
to effectively reduce the computational cost of these models without affecting
their accuracy. Finally, we conduct extensive analyses and ablation experiments
to characterize the behavior of the proposed models.
