Previous dialogue summarization techniques adapt large language models
pretrained on the narrative text by injecting dialogue-specific features into
the models. These features either require additional knowledge to recognize or
make the resulting models harder to tune. To bridge the format gap between
dialogues and narrative summaries in dialogue summarization tasks, we propose
to post-train pretrained language models (PLMs) to rephrase from dialogue to
narratives. After that, the model is fine-tuned for dialogue summarization as
usual. Comprehensive experiments show that our approach significantly improves
vanilla PLMs on dialogue summarization and outperforms other SOTA models by the
summary quality and implementation costs.

该论文提出了一种基于预训练语言模型转换对话到叙述语言的技术，利用此技术可以显著提高对话摘要的性能，而且具有低的实施成本。

使用伪重述的方式进行后训练对话摘要

Post-Training Dialogue Summarization using Pseudo-Paraphrasing

Understanding narrative text requires capturing characters' motivations,
goals, and mental states. This paper proposes an Entity-based Narrative Graph
(ENG) to model the internal-states of characters in a story. We explicitly
model entities, their interactions and the context in which they appear, and
learn rich representations for them. We experiment with different task-adaptive
pre-training objectives, in-domain training, and symbolic inference to capture
dependencies between different decisions in the output space. We evaluate our
model on two narrative understanding tasks: predicting character mental states,
and desire fulfillment, and conduct a qualitative analysis.

本文提出了一种实体为基础的叙述图（ENG），能够更好地建模故事中角色的内部状态，包括他们的动机、目标和心理状态，并通过实验和定量分析来评估其在预测角色心理状态和愿望实现任务中的表现。

使用基于实体的叙事图对人类心理状态进行建模

Modeling Human Mental States with an Entity-based Narrative Graph

Third-person fictional narrative text is composed not only of passages that
objectively narrate events, but also of passages that present characters'
thoughts, perceptions, and inner states. Such passages take a character's
``psychological point of view''. A language understander must determine the
current psychological point of view in order to distinguish the beliefs of the
characters from the facts of the story, to correctly attribute beliefs and
other attitudes to their sources, and to understand the discourse relations
among sentences. Tracking the psychological point of view is not a trivial
problem, because many sentences are not explicitly marked for point of view,
and whether the point of view of a sentence is objective or that of a character
(and if the latter, which character it is) often depends on the context in
which the sentence appears. Tracking the psychological point of view is the
problem addressed in this work. The approach is to seek, by extensive
examinations of naturally-occurring narrative, regularities in the ways that
authors manipulate point of view, and to develop an algorithm that tracks point
of view on the basis of the regularities found. This paper presents this
algorithm, gives demonstrations of an implemented system, and describes the
results of some preliminary empirical studies, which lend support to the
algorithm.

本文探讨通过算法追踪小说文字中的心理视角问题。通过对自然出现的叙述的详细研究，发现了作者操纵视角的几种规律，并提出了一种算法来跟踪文章中的视角。作者通过实验研究表明了这种算法的有效性。