  Salience Estimation aims to predict term importance in documents. Due to few
existing human-annotated datasets and the subjective notion of salience,
previous studies typically generate pseudo-ground truth for evaluation.
However, our investigation reveals that the evaluation protocol proposed by
prior work is difficult to replicate, thus leading to few follow-up studies
existing. Moreover, the evaluation process is problematic: the entity linking
tool used for entity matching is very noisy, while the ignorance of event
argument for event evaluation leads to boosted performance. In this work, we
propose a light yet practical entity and event salience estimation evaluation
protocol, which incorporates the more reliable syntactic dependency parser.
Furthermore, we conduct a comprehensive analysis among popular entity and event
definition standards, and present our own definition for the Salience
Estimation task to reduce noise during the pseudo-ground truth generation
process. Furthermore, we construct dependency-based heterogeneous graphs to
capture the interactions of entities and events. The empirical results show
that both baseline methods and the novel GNN method utilizing the heterogeneous
graph consistently outperform the previous SOTA model in all proposed metrics.
