Identifying the salience (i.e. importance) of discourse units is an important
task in language understanding. While events play important roles in text
documents, little research exists on analyzing their saliency status. This
paper empirically studies the Event Salience task and proposes two salience
detection models based on content similarities and discourse relations. The
first is a feature based salience model that incorporates similarities among
discourse units. The second is a neural model that captures more complex
relations between discourse units. Tested on our new large-scale event salience
corpus, both methods significantly outperform the strong frequency baseline,
while our neural model further improves the feature based one by a large
margin. Our analyses demonstrate that our neural model captures interesting
connections between salience and discourse unit relations (e.g., scripts and
frame structures).

本研究旨在研究事件显著性（重要性）并提出两个基于内容相似性和语篇关系的显著性检测模型，经测试，两种方法明显优于强频率基线，同时神经模型通过大幅提高基于特征的模型而进一步改善。