  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.
