Automated plot generation is the challenge of generating a sequence of events
that will be perceived by readers as the plot of a coherent story. Traditional
symbolic planners plan a story from a goal state and guarantee logical causal
plot coherence but rely on a library of hand-crafted actions with their
preconditions and effects. This closed world setting limits the length and
diversity of what symbolic planners can generate. On the other hand,
pre-trained neural language models can generate stories with great diversity,
while being generally incapable of ending a story in a specified manner and can
have trouble maintaining coherence. In this paper, we present an approach to
story plot generation that unifies causal planning with neural language models.
We propose to use commonsense knowledge extracted from large language models to
recursively expand a story plot in a backward chaining fashion. Specifically,
our system infers the preconditions for events in the story and then events
that will cause those conditions to become true. We performed automatic
evaluation to measure narrative coherence as indicated by the ability to answer
questions about whether different events in the story are causally related to
other events. Results indicate that our proposed method produces more coherent
plotlines than several strong baselines.

本文提出了一种将因果规划与神经语言模型统一起来的故事情节生成方法，该方法利用从大型语言模型中提取的常识知识以递归反向链接的方式扩展故事情节，并通过自动化评估证明相对于其他强基线方法它能生成更具连贯性的情节。

神经故事规划

Neural Story Planning

To improve the performance of long text generation, recent studies have
leveraged automatically planned event structures (i.e. storylines) to guide
story generation. Such prior works mostly employ end-to-end neural generation
models to predict event sequences for a story. However, such generation models
struggle to guarantee the narrative coherence of separate events due to the
hallucination problem, and additionally the generated event sequences are often
hard to control due to the end-to-end nature of the models. To address these
challenges, we propose NGEP, an novel event planning framework which generates
an event sequence by performing inference on an automatically constructed event
graph and enhances generalisation ability through a neural event advisor. We
conduct a range of experiments on multiple criteria, and the results
demonstrate that our graph-based neural framework outperforms the
state-of-the-art (SOTA) event planning approaches, considering both the
performance of event sequence generation and the effectiveness on the
downstream task of story generation.

本研究提出一种基于自动构建事件图和神经事件顾问的事件规划框架，以提高长文本生成的性能，实验结果表明，该框架不仅在事件序列生成方面表现优异，而且在故事生成的下游任务上的效果也十分显著。

NGEP：基于图形的事件计划框架用于故事生成

NGEP: A Graph-based Event Planning Framework for Story Generation

Progress in summarizing long texts is inhibited by the lack of appropriate
evaluation frameworks. When a long summary must be produced to appropriately
cover the facets of that text, that summary needs to present a coherent
narrative to be understandable by a reader, but current automatic and human
evaluation methods fail to identify gaps in coherence. In this work, we
introduce SNaC, a narrative coherence evaluation framework rooted in
fine-grained annotations for long summaries. We develop a taxonomy of coherence
errors in generated narrative summaries and collect span-level annotations for
6.6k sentences across 150 book and movie screenplay summaries. Our work
provides the first characterization of coherence errors generated by
state-of-the-art summarization models and a protocol for eliciting coherence
judgments from crowd annotators. Furthermore, we show that the collected
annotations allow us to train a strong classifier for automatically localizing
coherence errors in generated summaries as well as benchmarking past work in
coherence modeling. Finally, our SNaC framework can support future work in long
document summarization and coherence evaluation, including improved
summarization modeling and post-hoc summary correction.

该研究提出了一种新的评估框架 SNaC，以解决长文本摘要中缺乏合适的评估方法的问题，并开发了一种数据标注方法，以收集 6.6k 个句子的故事连贯性注释。此外，研究人员展示了所收集的注释可以训练一个强大的分类器，用于自动定位生成摘要中的连贯性错误，并且可以支持未来对长文档摘要和连贯性评估的更好的建模和纠错。