FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Coarse-grained retrieval units theoretically can provide more relevant information but may contain redundant content, which could distract the retriever and language models in downstream tasks.
“Coarse-grained retrieval units theoretically can provide more relevant information for the problem, but they may also contain redundant content, which could distract the retriever and language models in downstream tasks [50], [87].”
From Retrieval-Augmented Generation for Large Language Models: A Survey (2023) · cites this paper as [87]
The Information Retrieval & Knowledge Representation and Retrieval-Augmented Intelligence Report Generation sections describe retrieval and its use in prompting, but do not discuss the size of retrieval units. Searches for coarse-grained units, redundant content, and distraction of retrievers or language models found no passage addressing that trade-off. (LLM, conf=0.74)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L., & Liang, P. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.353 · Cited by 2,473 · see citing works
The narrative arc: Revealing core narrative structures through text analysis
Boyd, R. L., Blackburn, K. G., & Pennebaker, J. W. (2020) · Science Advances · doi:10.1126/sciadv.aba2196 · Cited by 185 · see citing works
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
Ke, P., Ji, H., Ran, Y., Cui, X., Wang, L., Song, L., Zhu, X., & Huang, M. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.223 · Cited by 92 · see citing works
Flagged first, then by citations
Research works printed but not linked: 19
- Wendy G Lehnert. Plot units and narrative summarization. Cognitive science, 5(4):293-331, 1981.
- Priyanka Ranade, Sanorita Dey, Anupam Joshi, and Tim Finin. Com- putational understanding of narratives: A survey. IEEE Access, 2022.
- Kristin Thompson. Storytelling in the new Hollywood: Understanding classical narrative technique. Harvard University Press, 1999.
- Faten El Outa, Matteo Francia, Patrick Marcel, Veronika Peralta, and Panos Vassiliadis. Supporting the generation of data narratives. In ER Forum/Posters/Demos, pages 168-172, 2020.
- Xianshu Zhu and Tim Oates. Finding story chains in newswire articles. In 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pages 93-100. IEEE, 2012.
- Dafna Shahaf and Carlos Guestrin. Connecting the dots between news articles. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010.
- Gabriel Pui Cheong Fung, Jeffrey Xu Yu, Huan Liu, and Philip S Yu. Time-dependent event hierarchy construction. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 300-309, 2007.
- Carlo Meghini, Valentina Bartalesi, and Daniele Metilli. Representing narratives in digital libraries: The narrative ontology. Semantic Web, 12(2):241-264, 2021.
- Amit Singhal, Chris Buckley, and Manclar Mitra. Pivoted document length normalization. In Acm sigir forum, volume 51, pages 176-184. ACM New York, NY, USA, 2017.
- Nick Craswell, W Bruce Croft, Maarten de Rijke, Jiafeng Guo, and Bhaskar Mitra. Neural information retrieval: introduction to the special issue. Information Retrieval Journal, 21:107-110, 2018.
- Laura Dietz, Alexander Kotov, and Edgar Meij. Utilizing knowledge graphs for text-centric information retrieval. In The 41st international ACM SIGIR conference on research & development in information retrieval, pages 1387-1390, 2018.
- Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi. Text generation from knowledge graphs with graph transformers. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:, 2019.
- Xavier Schmitt, Sylvain Kubler, Jérémy Robert, Mike Papadakis, and Yves LeTraon. A replicable comparison study of ner software: Stanfordnlp, nltk, opennlp, spacy, gate. In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), pages 338-343. IEEE, 2019.
- Jakub Piskorski, Nicolas Stefanovitch, Giovanni Da San Martino, and Preslav Nakov. Semeval-2023 task 3: Detecting the category, the framing, and the persuasion techniques in online news in a multi- lingual setup. In Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 2343-2361, 2023.
- Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. The pile: An 800gb dataset of diverse text for language modeling. arXiv preprint arXiv:2101.00027, 2020. · arXiv
- Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. Retrieval augmentation reduces hallucination in conversation. arXiv preprint arXiv:2104.07567, 2021. · arXiv
- Chin-Yew Lin. Rouge: A package for automatic evaluation of sum- maries. In Text summarization branches out, pages 74-81, 2004.
- Mary L McHugh. Interrater reliability: the kappa statistic. Biochemia medica, 22(3):276-282, 2012.
- I Elaine Allen and Christopher A Seaman. Likert scales and data analyses. Quality progress, 40(7):64-65, 2007.
Cited by
Sorted by citations
Retrieval-Augmented Generation for Large Language Models: A Survey
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., & Wang, H. (2023) · arXiv · doi:10.48550/arxiv.2312.10997 · Cited by 876 · see citing works
“Lastly, there’s a risk of generation models overly depending on augmented information, potentially resulting in outputs that merely reiterate the retrieved content without providing new value or synthesized information.”
BackgroundThe AI Hippocampus: How Far are We From Human Memory?
Jia, Z., Li, J., Kang, Y., Wang, Y., Wu, T., Wang, Q., Wang, X., Zhang, S., Shen, J., Li, Q., et al. (2026) · arXiv · doi:10.48550/arxiv.2601.09113
- Authors
- Ranade, P., & Joshi, A.
- Affiliations
- University of Maryland, Baltimore County (US)
- Venue
- Proceedings of the International Conference on Advances in Social Networks Analysis and Mining
- Publisher
- ACM
- Published
- 2023-11-06
- Pages
- 603-610
- OpenAlex
- W4387928582
- Cited by
- 11
- References
- 3
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 3
- FWCI
- 14.94
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- Maryland Shared Open Access Repository (USMAI Consortium) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Social Sciences › Political Science and International Relations › Intelligence, Security, War Strategy
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Keywords
- narrative construction, retrieval-augmented generation, plot structure, information gap, intelligence analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
