Abstractive summarization for long-form narrative texts such as movie scripts
is challenging due to the computational and memory constraints of current
language models. A movie script typically comprises a large number of scenes;
however, only a fraction of these scenes are salient, i.e., important for
understanding the overall narrative. The salience of a scene can be
operationalized by considering it as salient if it is mentioned in the summary.
Automatically identifying salient scenes is difficult due to the lack of
suitable datasets. In this work, we introduce a scene saliency dataset that
consists of human-annotated salient scenes for 100 movies. We propose a
two-stage abstractive summarization approach which first identifies the salient
scenes in script and then generates a summary using only those scenes. Using
QA-based evaluation, we show that our model outperforms previous
state-of-the-art summarization methods and reflects the information content of
a movie more accurately than a model that takes the whole movie script as
input.

通过提取场景的显著性，针对电影剧本等长篇故事性文本进行抽象概括是具有挑战性的。本研究引入了一个包含 100 部电影人工标注显著场景的数据集，提出了一个两阶段的抽象概括方法，首先识别出剧本中的显著场景，然后利用这些场景生成摘要。基于问答的评估结果显示，我们的模型优于以往最先进的摘要方法，并且比将整个剧本作为输入的模型更准确地反映了电影的信息内容。

选择与总结：电影剧本摘要的场景显著性

Select and Summarize: Scene Saliency for Movie Script Summarization

When reading a story, humans can rapidly understand new fictional characters
with a few observations, mainly by drawing analogy to fictional and real people
they met before in their lives. This reflects the few-shot and meta-learning
essence of humans' inference of characters' mental states, i.e., humans'
theory-of-mind (ToM), which is largely ignored in existing research. We fill
this gap with a novel NLP benchmark, TOM-IN-AMC, the first assessment of
models' ability of meta-learning of ToM in a realistic narrative understanding
scenario. Our benchmark consists of $\sim$1,000 parsed movie scripts for this
purpose, each corresponding to a few-shot character understanding task; and
requires models to mimic humans' ability of fast digesting characters with a
few starting scenes in a new movie. Our human study verified that humans can
solve our problem by inferring characters' mental states based on their
previously seen movies; while the state-of-the-art metric-learning and
meta-learning approaches adapted to our task lags 30% behind.

本研究填补现有研究中忽略的人类理解角色的心理状态（即人类心智理论）的元学习本质，创造了一个基于电影脚本的语言理解基准评估，要求模型在新电影的几个初始场景中快速理解角色，我们的人类研究验证了人类可以推理角色的心理状态，但目前的度量学习和元学习方法在任务上落后于人类 30%。

以电影中的少样本人物理解为评估元学习理论

Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind

We investigate a new commonsense inference task: given an event described in
a short free-form text ("X drinks coffee in the morning"), a system reasons
about the likely intents ("X wants to stay awake") and reactions ("X feels
alert") of the event's participants. To support this study, we construct a new
crowdsourced corpus of 25,000 event phrases covering a diverse range of
everyday events and situations. We report baseline performance on this task,
demonstrating that neural encoder-decoder models can successfully compose
embedding representations of previously unseen events and reason about the
likely intents and reactions of the event participants. In addition, we
demonstrate how commonsense inference on people's intents and reactions can
help unveil the implicit gender inequality prevalent in modern movie scripts.

研究探讨了一种新的常识推断任务，通过构建新的众包语料库并使用神经编码器 - 解码器模型，成功地分析先前未见过的事件并推断参与者的意图和反应，揭示了现代电影剧本中普遍存在的隐性性别不平等问题。