Narrative construction is the process of representing disparate event information into a logical plot structure that models an end to end story. Intelligence analysis is an example of a domain that can benefit tremendously from narrative construction techniques, particularly in aiding analysts during the largely manual and costly process of synthesizing event information into comprehensive intelligence reports. Manual intelligence report generation is often prone to challenges such as integrating dynamic event information, writing fine-grained queries, and closing information gaps. This motivates the development of a system that retrieves and represents critical aspects of events in a form that aids in automatic generation of intelligence reports. We introduce a Retrieval Augmented Generation (RAG) approach to augment prompting of an autoregressive decoder by retrieving structured information asserted in a knowledge graph to generate targeted information based on a narrative plot model. We apply our approach to the problem of neural intelligence report generation and introduce FABULA, framework to augment intelligence analysis workflows using RAG. An analyst can use FABULA to query an Event Plot Graph (EPG) to retrieve relevant event plot points, which can be used to augment prompting of a Large Language Model (LLM) during intelligence report generation. Our evaluation studies show that the plot points included in the generated intelligence reports have high semantic relevance, high coherency, and low data redundancy.

翻译：叙事构建是将分散的事件信息整合为逻辑情节结构以形成完整故事的过程。情报分析是能极大受益于叙事构建技术的典型领域，尤其在辅助分析人员完成人工且成本高昂的事件信息综合成情报报告的过程中。人工情报报告生成常面临动态事件信息整合、细粒度查询编写及信息缺口填补等挑战。这促使我们开发一种系统，能够以辅助自动生成情报报告的形式检索并表征事件关键要素。我们提出一种检索增强生成（RAG）方法，通过检索知识图谱中断言的结构化信息来增强自回归解码器的提示能力，从而基于叙事情节模型生成目标信息。我们将该方法应用于神经情报报告生成问题，并引入FABULA框架，利用RAG增强情报分析工作流。分析人员可使用FABULA查询事件情节图（EPG）以检索相关事件情节节点，这些节点可在情报报告生成过程中用于增强大语言模型（LLM）的提示。评估研究表明，生成的情报报告中包含的情节节点具有高语义相关性、高连贯性和低数据冗余度。