We present a theory-inspired visual narrative generator that incorporates
comic-authoring idioms, which transfers the conceptual principles of comics
into system layers that integrate the theories to create comic content. The
generator creates comics through sequential decision-making across layers from
panel composition, object positions, panel transitions, and narrative elements.
Each layer's decisions are based on narrative goals and follow the respective
layer idioms of the medium. Cohn's narrative grammar provides the overall story
arc. Photographic compositions inspired by the rule of thirds is used to
provide panel compositions. McCloud's proposed panel transitions based on focus
shifts between scene, character, and temporal changes are encoded in the
transition layer. Finally, common overlay symbols (such as the exclamation) are
added based on analyzing action verbs using an action-verb ontology. We
demonstrate the variety of generated comics through various settings with
example outputs. The generator and associated modules could be a useful system
for visual narrative authoring and for further research into computational
models of visual narrative understanding.

我们提出了一种基于理论启发的视觉叙事生成器，该生成器融合了漫画创作习语，将漫画的概念原则转化为整合理论的系统层次，用于创作漫画内容。生成器通过从面板构图、物体位置、面板转换和叙事要素的层次上进行顺序决策来创建漫画。每个层次的决策基于叙事目标，遵循媒体的相应层次习语。借鉴 Cohn 的叙事语法提供整体故事弧线。面板构图采用受三分法启发的照片构图。基于 McCloud 提出的面板转换理论，包括场景、人物和时间变化之间的焦点转移的转换层被编码进系统中。最后，基于对动作动词使用动作动词本体论进行分析，添加常见的叠加符号（例如感叹号）。通过各种设置和示例输出展示了生成的漫画的多样性。该生成器和相关模块可以用于视觉叙事创作，并进一步研究视觉叙事理解的计算模型。

漫画风格视觉叙事定制生成器

A Customizable Generator for Comic-Style Visual Narrative

Automatic extraction of narrative elements from text, combining narrative
theories with computational models, has been receiving increasing attention
over the last few years. Previous works have utilized the oral narrative theory
by Labov and Waletzky to identify various narrative elements in personal
stories texts. Instead, we direct our focus to informational texts,
specifically news stories. We introduce NEAT (Narrative Elements AnnoTation) -
a novel NLP task for detecting narrative elements in raw text. For this
purpose, we designed a new multi-label narrative annotation scheme, better
suited for informational text (e.g. news media), by adapting elements from the
narrative theory of Labov and Waletzky (Complication and Resolution) and adding
a new narrative element of our own (Success). We then used this scheme to
annotate a new dataset of 2,209 sentences, compiled from 46 news articles from
various category domains. We trained a number of supervised models in several
different setups over the annotated dataset to identify the different narrative
elements, achieving an average F1 score of up to 0.77. The results demonstrate
the holistic nature of our annotation scheme as well as its robustness to
domain category.

本研究介绍了一种名为 NEAT 的新型 NLP 任务，针对新闻等信息文本，基于 Labov 和 Waletzky 的叙事理论设计了一种新的多标签叙事注释方案，标注了从多种类别域收集的 46 篇新闻文章中的 2,209 句子，并训练了多个监督模型进行不同设置的分类，成功地识别了不同的叙事元素，并达到了高达 0.77 的 F1 分数，表明该研究具有很好的全面性及对域类别的鲁棒性。

在信息文本中检测叙事元素

Detecting Narrative Elements in Informational Text

Stories are a very compelling medium to convey ideas, experiences, social and
cultural values. Narrative is a specific manifestation of the story that turns
it into knowledge for the audience. In this paper, we propose a machine
learning approach to capture the narrative elements in movies by bridging the
gap between the low-level data representations and semantic aspects of the
visual medium. We present a Memory-Augmented Video Semantic Network, called
Video SemNet, to encode the semantic descriptors and learn an embedding for the
video. The model employs two main components: (i) a neural semantic learner
that learns latent embeddings of semantic descriptors and (ii) a memory module
that retains and memorizes specific semantic patterns from the video. We
evaluate the video representations obtained from variants of our model on two
tasks: (a) genre prediction and (b) IMDB Rating prediction. We demonstrate that
our model is able to predict genres and IMDB ratings with a weighted F-1 score
of 0.72 and 0.63 respectively. The results are indicative of the
representational power of our model and the ability of such representations to
measure audience engagement.

该研究通过提出一种机器学习方法，介绍了一种记忆增强视频语义网络来捕捉电影中的叙事元素，从而在两个任务中成功预测电影的流派和 IMDB 评分。