mlsanityData to 14 Oct 2026

Multigenre AI-powered Story Composition

arXiv 2405.06685 ↗First posted 6 May 2024cs.CLcited by 1

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Abstract (arXiv)

This paper shows how to construct genre patterns, whose purpose is to guide interactive story composition in a way that enforces thematic consistency. To start the discussion we argue, based on previous seminal works, for the existence of five fundamental genres, namely comedy, romance - in the sense of epic plots, flourishing since the twelfth century -, tragedy, satire, and mystery. To construct the patterns, a simple two-phase process is employed: first retrieving examples that match our genre characterizations, and then applying a form of most specific generalization to the groups of examples in order to find their commonalities. In both phases, AI agents are instrumental, with our PatternTeller prototype being called to operate the story composition process, offering the opportunity to generate stories from a given premise of the user, to be developed under the guidance of the chosen pattern and trying to accommodate the user's suggestions along the composition stages.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 72 references that are in the map. First posted on arXiv 6 May 2024.

Builds on

7 of 72 references.

References · 7

  1. 2018Hierarchical Neural Story Generationcited by 1,193
  2. 2022Re3: Generating Longer Stories With Recursive Reprompting and Revisioncited by 193
  3. 2020MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Modelscited by 74
  4. 2024Creating Suspenseful Stories: Iterative Planning with Large Language Modelscited by 22
  5. 2020Narrative Interpolation for Generating and Understanding Storiescited by 19
  6. 2021Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learningcited by 14
  7. 2022TropeTwist: Trope-based Narrative Structure Generationcited by 8

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Citing papers per year: 2024: 1.

1 paper.