TropeTwist: Trope-based Narrative Structure Generation
- URL: http://arxiv.org/abs/2204.09672v1
- Date: Thu, 31 Mar 2022 16:02:17 GMT
- Title: TropeTwist: Trope-based Narrative Structure Generation
- Authors: Alberto Alvarez, Jose Font
- Abstract summary: We present TropeTwist, a trope-based system that can describe narrative structures in games in a more abstract and generic level.
To demonstrate the system, we represent the narrative structure of three different games.
We use MAP-Elites to generate and evaluate novel quality-diverse narrative graphs encoded as graph grammars.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Games are complex, multi-faceted systems that share common elements and
underlying narratives, such as the conflict between a hero and a big bad enemy
or pursuing some goal that requires overcoming challenges. However, identifying
and describing these elements together is non-trivial as they might differ in
certain properties and how players might encounter the narratives. Likewise,
generating narratives also pose difficulties when encoding, interpreting,
analyzing, and evaluating them. To address this, we present TropeTwist, a
trope-based system that can describe narrative structures in games in a more
abstract and generic level, allowing the definition of games' narrative
structures and their generation using interconnected tropes, called narrative
graphs. To demonstrate the system, we represent the narrative structure of
three different games. We use MAP-Elites to generate and evaluate novel
quality-diverse narrative graphs encoded as graph grammars, using these three
hand-made narrative structures as targets. Both hand-made and generated
narrative graphs are evaluated based on their coherence and interestingness,
which are improved through evolution.
Related papers
- Agents' Room: Narrative Generation through Multi-step Collaboration [54.98886593802834]
We propose a generation framework inspired by narrative theory that decomposes narrative writing into subtasks tackled by specialized agents.
We show that Agents' Room generates stories preferred by expert evaluators over those produced by baseline systems.
arXiv Detail & Related papers (2024-10-03T15:44:42Z) - Generating Visual Stories with Grounded and Coreferent Characters [63.07511918366848]
We present the first model capable of predicting visual stories with consistently grounded and coreferent character mentions.
Our model is finetuned on a new dataset which we build on top of the widely used VIST benchmark.
We also propose new evaluation metrics to measure the richness of characters and coreference in stories.
arXiv Detail & Related papers (2024-09-20T14:56:33Z) - Mapping News Narratives Using LLMs and Narrative-Structured Text Embeddings [0.0]
We introduce a numerical narrative representation grounded in structuralist linguistic theory.
We extract the actants using an open-source LLM and integrate them into a Narrative-Structured Text Embedding.
We demonstrate the analytical insights of the method on the example of 5000 full-text news articles from Al Jazeera and The Washington Post on the Israel-Palestine conflict.
arXiv Detail & Related papers (2024-09-10T14:15:30Z) - Are Large Language Models Capable of Generating Human-Level Narratives? [114.34140090869175]
This paper investigates the capability of LLMs in storytelling, focusing on narrative development and plot progression.
We introduce a novel computational framework to analyze narratives through three discourse-level aspects.
We show that explicit integration of discourse features can enhance storytelling, as is demonstrated by over 40% improvement in neural storytelling.
arXiv Detail & Related papers (2024-07-18T08:02:49Z) - 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) - GENEVA: GENErating and Visualizing branching narratives using LLMs [15.43734266732214]
textbfGENEVA, a prototype tool, generates a rich narrative graph with branching and reconverging storylines.
textbfGENEVA has the potential to assist in game development, simulations, and other applications with game-like properties.
arXiv Detail & Related papers (2023-11-15T18:55:45Z) - Discovering collective narratives shifts in online discussions [3.6231158294409482]
We propose a systematic narrative discovery framework that fills the gap by combining change point detection, semantic role labeling (SRL), and automatic aggregation of narrative fragments into narrative networks.
We evaluate our model with synthetic and empirical data two-Twitter corpora about COVID-19 and 2017 French Election.
Results demonstrate that our approach can recover major narrative shifts that correspond to the major events.
arXiv Detail & Related papers (2023-07-17T15:00:04Z) - Story Designer: Towards a Mixed-Initiative Tool to Create Narrative
Structures [4.4447051343759965]
This paper presents Story Designer, a mixed-initiative co-creative narrative structure tool built on top of the Evolutionary Dungeon Designer (EDD)
Story Designer uses tropes as building blocks for narrative designers to compose complete narrative structures by interconnecting them in graph structures called narrative graphs.
At the same time, we use the levels designed within EDD as constraints for the narrative structure, intertwining both level design and narrative.
arXiv Detail & Related papers (2022-10-11T16:11:32Z) - Narrative Maps: An Algorithmic Approach to Represent and Extract
Information Narratives [6.85316573653194]
This article combines the theory of narrative representations with the data from modern online systems.
A narrative map representation illustrates the events and stories in the narrative as a series of landmarks and routes on the map.
Our findings have implications for intelligence analysts, computational journalists, and misinformation researchers.
arXiv Detail & Related papers (2020-09-09T18:30:44Z) - PlotMachines: Outline-Conditioned Generation with Dynamic Plot State
Tracking [128.76063992147016]
We present PlotMachines, a neural narrative model that learns to transform an outline into a coherent story by tracking the dynamic plot states.
In addition, we enrich PlotMachines with high-level discourse structure so that the model can learn different writing styles corresponding to different parts of the narrative.
arXiv Detail & Related papers (2020-04-30T17:16:31Z) - 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.