Events in a narrative differ in salience: some are more important to the
story than others. Estimating event salience is useful for tasks such as story
generation, and as a tool for text analysis in narratology and folkloristics.
To compute event salience without any annotations, we adopt Barthes' definition
of event salience and propose several unsupervised methods that require only a
pre-trained language model. Evaluating the proposed methods on folktales with
event salience annotation, we show that the proposed methods outperform
baseline methods and find fine-tuning a language model on narrative texts is a
key factor in improving the proposed methods.

本文提出了几种无监督方法来计算事件显著性，这些方法采用了巴特思对事件显著性的定义，并仅需要一个预先训练的语言模型。 在叙事文本上微调语言模型是提高提出方法表现的关键因素。

运用巴特的基本功能模拟故事情节中事件的重要性

Modeling Event Salience in Narratives via Barthes' Cardinal Functions

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。