# FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction

arXiv: https://arxiv.org/abs/2310.13848 · HTML: https://mlsanity.com/paper/2310.13848/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Placed in

- Retrieval-augmented generation / [Knowledge-graph and structured-knowledge RAG](https://mlsanity.com/rag/knowledge-graph-and-structured-knowledge-rag/)

## Builds on (most cited first, 5 of 5)

- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Text Generation from Knowledge Graphs with Graph Transformers](https://mlsanity.com/paper/1904.02342/) (2019)
- [JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs](https://mlsanity.com/paper/2106.10502/) (2021)

## Built on by (most cited first, 10 of 10)

- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Graph Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2408.08921/) (2024)
- [Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey](https://mlsanity.com/paper/2504.10499/) (2025)
- [Efficient Knowledge Infusion via KG-LLM Alignment](https://mlsanity.com/paper/2406.03746/) (2024)
- [LexDrafter: Terminology Drafting for Legislative Documents using Retrieval Augmented Generation](https://mlsanity.com/paper/2403.16295/) (2024)
- [Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery](https://mlsanity.com/paper/2506.08771/) (2025)
- [XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation](https://mlsanity.com/paper/2506.13782/) (2025)
- [On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection](https://mlsanity.com/paper/2510.10456/) (2025)
- [KEO: Knowledge Extraction on OMIn via Knowledge Graphs and RAG for Safety-Critical Aviation Maintenance](https://mlsanity.com/paper/2510.05524/) (2025)

## Abstract (arXiv)

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.