Narrative is a ubiquitous component of human communication. Understanding its
structure plays a critical role in a wide variety of applications, ranging from
simple comparative analyses to enhanced narrative retrieval, comprehension, or
reasoning capabilities. Prior research in narratology has highlighted the
importance of studying the links between cognitive and linguistic aspects of
narratives for effective comprehension. This interdependence is related to the
textual semantics and mental language in narratives, referring to characters'
motivations, feelings or emotions, and beliefs. However, this interdependence
is hardly explored for modeling narratives. In this work, we propose the task
of automatically detecting prominent elements of the narrative structure by
analyzing the role of characters' inferred mental state along with linguistic
information at the syntactic and semantic levels. We introduce a STORIES
dataset of short personal narratives containing manual annotations of key
elements of narrative structure, specifically climax and resolution. To this
end, we implement a computational model that leverages the protagonist's mental
state information obtained from a pre-trained model trained on social
commonsense knowledge and integrates their representations with contextual
semantic embed-dings using a multi-feature fusion approach. Evaluating against
prior zero-shot and supervised baselines, we find that our model is able to
achieve significant improvements in the task of identifying climax and
resolution.

本文提出了一种计算模型，通过分析角色猜测的心理状态和句法和语义层面的语言信息，自动检测叙述结构的主要元素。通过使用预先训练的社交常识知识模型获取主角的心理状态信息，并使用多功能融合方法将其与语境语义嵌入相结合，我们建立了一个包含手动注释的 STORIES 数据集的计算模型来实现这一任务，并在评估中发现我们的模型能够实现极显著的改进来识别高潮和结局。

M-SENSE：利用主角心理再现建模短篇个人叙述中的叙事结构

M-SENSE: Modeling Narrative Structure in Short Personal Narratives Using Protagonist's Mental Representations

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。