← Back to CNS 2.0 Research Roadmap: A Multi-Year Vision

Annotated Bibliography

A curated list of the foundational papers and research that underpin the Chiral Narrative Synthesis 2.0 project.

Annotated Bibliography

Chiral Narrative Synthesis (CNS) 2.0 integrates decades of research across multiple domains. This annotated bibliography highlights the foundational works and key papers that provide the theoretical and computational pillars for the project. The papers are grouped by the six core research thrusts that inform the CNS framework.

1. Argumentation Mining & Computational Argumentation

This field provides the tools to extract, structure, and evaluate arguments, which is central to the SNO’s ReasoningGraph and the LogicCritic.

  • Dung, P. M. (1995). On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence, 77(2), 321-358.

    Annotation: This is arguably the most important foundational paper for computational argumentation. Dung’s work on Abstract Argumentation Frameworks (AAFs) provides the formal mathematical basis for determining which arguments can be collectively accepted from a set of conflicting statements. This directly inspires the acceptability semantics of our ReasoningGraph and the core logic of the DialecticalSynthesis engine.

  • Walton, D. (1996). Argumentation Schemes for Presumptive Reasoning. Lawrence Erlbaum Associates.

    Annotation: Walton’s work provides a taxonomy of common, real-world reasoning patterns (e.g., argument from expert opinion, argument from analogy). These schemes are crucial for the LogicCritic, providing a bridge between formal logic and the often-messy, presumptive reasoning found in natural language, allowing the system to identify common fallacies.

  • Lawrence, J., & Reed, C. (2019). Argument Mining: A Survey. Computational Linguistics, 45(4), 765-813.

  • Wachsmuth, H., et al. (2017). Computational Argumentation Quality Assessment: A Survey. Computational Linguistics, 43(4), 839-873.

2. Multi-Agent Systems & Computational Dialectics

This research provides the architectural blueprint for modeling the interactions between conflicting narratives as a society of rational agents engaged in structured dialogue.

  • Rahwan, I., & Simari, G. R. (Eds.). (2009). Argumentation in Artificial Intelligence. Springer.

    Annotation: This comprehensive volume serves as a bridge between abstract argumentation and multi-agent systems. It provides numerous models for how autonomous agents can use argumentation to persuade, negotiate, and deliberate, directly informing the design of the agent-based operational loop and the protocols for the GenerativeSynthesisEngine.

  • McBurney, P., & Parsons, S. (2002). Games that agents play: A formal framework for dialogues between autonomous agents. Journal of Logic, Language and Information, 11(3), 315-342.

  • Sabater, J., & Sierra, C. (2005). Review on computational trust and reputation models. Artificial Intelligence Review, 24(1), 59-88.

3. Knowledge Graph (KG) Fusion & Conflict Resolution

KGs provide the factual backbone for grounding narratives in verifiable data. Techniques from this field are essential for managing the EvidenceSet and resolving factual contradictions.

  • Hogan, A., et al. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1-37.

    Annotation: This survey provides a modern, comprehensive overview of Knowledge Graph technology, from data models to reasoning. It establishes the technical foundation for using KGs as the grounding layer for the EvidenceSet and informs the design of the GroundingCritic.

  • Paulheim, H. (2017). Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web, 8(3), 427-442.

  • Euzenat, J., & Shvaiko, P. (2013). Ontology Matching (2nd ed.). Springer.

4. Contradiction Detection, Stance Detection, and NLI

These NLP tasks are the engine for identifying the “chiral pairs” of narratives that are the primary input for synthesis.

  • Thorne, J., et al. (2018). FEVER: A Large-scale Dataset for Fact Extraction and VERification. Proceedings of NAACL-HLT.

    Annotation: The FEVER dataset and the associated task were pivotal in advancing automated fact-checking. The techniques developed for this task are directly relevant to the GroundingCritic, providing a blueprint for how to verify claims in an SNO’s ReasoningGraph against the EvidenceSet.

  • de Marneffe, M.-C., et al. (2008). Finding contradictions in text. Proceedings of ACL-08: HLT.

  • Bowman, S. R., et al. (2015). A large annotated corpus for learning natural language inference. Proceedings of EMNLP.

5. Multi-Document Summarization & Viewpoint Analysis

This field provides the techniques for generating the final, coherent output of the synthesis process, ensuring that it is concise and represents the key aspects of the input narratives.

  • Barzilay, R., & McKeown, K. R. (2005). Sentence fusion for multi-document summarization. Computational Linguistics, 31(3), 297-328.
  • Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.

6. Philosophy of Science & Epistemology

This philosophical grounding provides the “why” behind CNS 2.0. It defines the nature of knowledge, belief, and justification that the system attempts to model computationally.

  • Gärdenfors, P. (1988). Knowledge in Flux: Modeling the Dynamics of Epistemic States. MIT Press.

    Annotation: This seminal work in belief revision theory provides the formal principles for how a rational agent should update its beliefs when faced with new information that contradicts its existing knowledge. The entire CNS 2.0 operational loop—where new, high-scoring SNOs replace older ones—is a computational implementation of the AGM (Alchourrón, Gärdenfors, Makinson) postulates described in this book.

  • Kuhn, T. S. (1962). The Structure of Scientific Revolutions. University of Chicago Press.

  • Toulmin, S. E. (2003). The Uses of Argument. Cambridge University Press.