Planning Beyond Text: Graph-based Reasoning for Complex Narrative Generation
- URL: http://arxiv.org/abs/2604.21253v1
- Date: Thu, 23 Apr 2026 03:45:59 GMT
- Title: Planning Beyond Text: Graph-based Reasoning for Complex Narrative Generation
- Abstract summary: We introduce PLOTTER, a framework that performs narrative planning on structural graph representations.<n>PLOTTER executes the Evaluate-Plan-Revise cycle on the event graph and character graph.<n> Experiments demonstrate that PLOTTER significantly outperforms representative baselines across diverse narrative scenarios.
- Score: 19.553521589272012
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While LLMs demonstrate remarkable fluency in narrative generation, existing methods struggle to maintain global narrative coherence, contextual logical consistency, and smooth character development, often producing monotonous scripts with structural fractures. To this end, we introduce PLOTTER, a framework that performs narrative planning on structural graph representations instead of the direct sequential text representations used in existing work. Specifically, PLOTTER executes the Evaluate-Plan-Revise cycle on the event graph and character graph. By diagnosing and repairing issues of the graph topology under rigorous logical constraints, the model optimizes the causality and narrative skeleton before complete context generation. Experiments demonstrate that PLOTTER significantly outperforms representative baselines across diverse narrative scenarios. These findings verify that planning narratives on structural graph representations-rather than directly on text-is crucial to enhance the long context reasoning of LLMs in complex narrative generation.
Related papers
- LLM as Graph Kernel: Rethinking Message Passing on Text-Rich Graphs [18.509779826302196]
We introduce RAMP, a Raw-text Anchored Message Passing approach to text-rich graphs.<n>RAMP exploits the text-rich nature of the graph via a novel dual-representation scheme.<n>Extensive experiments show that RAMP effectively bridges the gap between graph propagation and deep text reasoning.
arXiv Detail & Related papers (2026-03-16T07:40:09Z) - Creative Convergence or Imitation? Genre-Specific Homogeneity in LLM-Generated Chinese Literature [17.985893852336865]
We propose a novel theoretical framework for analysis by incorporating Proppian narratology and narrative functions.<n>We extend Propp's narrative theory, defining 34 narrative functions suited to modern web narrative structures.<n> Experiments reveal that the primary reasons for the singular narrative logic and severe homogenization in generated texts are that current LLMs are unable to correctly comprehend the meanings of narrative functions.
arXiv Detail & Related papers (2026-03-15T15:15:48Z) - EvidFuse: Writing-Time Evidence Learning for Consistent Text-Chart Data Reporting [31.185433601906738]
We propose textbfEvidFuse, a training-free multi-agent framework for writing-time text-chart interleaved generation for data-driven reports.<n>EvidFuse decouples visualization analysis from long-form drafting via two collaborating components.<n>It attains the top rank in both LLM-as-a-judge and human evaluations on chart quality, chart-text alignment, and report-level usefulness.
arXiv Detail & Related papers (2026-01-09T02:41:54Z) - Disco-RAG: Discourse-Aware Retrieval-Augmented Generation [81.53888908988756]
We propose Disco-RAG, a discourse-aware framework that injects discourse signals into the generation process.<n>Our method constructs intra-chunk discourse trees to capture local hierarchies and builds inter-chunk rhetorical graphs to model cross-passage coherence.<n>Experiments on question answering and long-document summarization benchmarks show the efficacy of our approach.
arXiv Detail & Related papers (2026-01-07T20:32:50Z) - Graph-O1 : Monte Carlo Tree Search with Reinforcement Learning for Text-Attributed Graph Reasoning [7.792321858331646]
Graph-O1 is an agentic GraphRAG framework that enables LLMs to conduct stepwise, interactive reasoning over graphs.<n>Our approach integrates Monte Carlo Tree Search (MCTS) with end-to-end reinforcement learning, allowing the model to explore and retrieve only the most informative subgraph components.
arXiv Detail & Related papers (2025-11-26T21:32:04Z) - CREFT: Sequential Multi-Agent LLM for Character Relation Extraction [17.568992245453224]
CREFT builds a base character graph through knowledge distillation, then iteratively refines character composition, relation extraction, role identification, and group assignments.<n> Experiments on a curated Korean drama dataset demonstrate that CREFT significantly outperforms single-agent LLM baselines in both accuracy and completeness.
arXiv Detail & Related papers (2025-05-30T13:01:36Z) - GPT4SGG: Synthesizing Scene Graphs from Holistic and Region-specific Narratives [69.36723767339001]
We propose a novel framework named textitGPT4SGG to obtain more accurate and comprehensive scene graph signals.
We show textitGPT4SGG significantly improves the performance of SGG models trained on image-caption data.
arXiv Detail & Related papers (2023-12-07T14:11:00Z) - EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form
Narrative Text Generation [114.50719922069261]
We propose a new framework called Evaluation-guided Iterative Plan Extraction for long-form narrative text generation (EIPE-text)
EIPE-text has three stages: plan extraction, learning, and inference.
We evaluate the effectiveness of EIPE-text in the domains of novels and storytelling.
arXiv Detail & Related papers (2023-10-12T10:21:37Z) - Visual Storytelling with Question-Answer Plans [70.89011289754863]
We present a novel framework which integrates visual representations with pretrained language models and planning.
Our model translates the image sequence into a visual prefix, a sequence of continuous embeddings which language models can interpret.
It also leverages a sequence of question-answer pairs as a blueprint plan for selecting salient visual concepts and determining how they should be assembled into a narrative.
arXiv Detail & Related papers (2023-10-08T21:45:34Z) - Scene Graph Modification as Incremental Structure Expanding [61.84291817776118]
We focus on scene graph modification (SGM), where the system is required to learn how to update an existing scene graph based on a natural language query.
We frame SGM as a graph expansion task by introducing the incremental structure expanding (ISE)
We construct a challenging dataset that contains more complicated queries and larger scene graphs than existing datasets.
arXiv Detail & Related papers (2022-09-15T16:26:14Z) - Screenplay Summarization Using Latent Narrative Structure [78.45316339164133]
We propose to explicitly incorporate the underlying structure of narratives into general unsupervised and supervised extractive summarization models.
We formalize narrative structure in terms of key narrative events (turning points) and treat it as latent in order to summarize screenplays.
Experimental results on the CSI corpus of TV screenplays, which we augment with scene-level summarization labels, show that latent turning points correlate with important aspects of a CSI episode.
arXiv Detail & Related papers (2020-04-27T11:54:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.