Towards a Formal Model of Narratives

• URL: http://arxiv.org/abs/2103.12872v1
• Date: Tue, 23 Mar 2021 22:33:23 GMT
• Title: Towards a Formal Model of Narratives
• Authors: Louis Castricato and Stella Biderman and Rogelio E. Cardona-Rivera and David Thue
• Abstract summary: Our framework affords the ability to discuss key qualities of stories and their communication. We demonstrate its applicability to computational narratology by giving explicit algorithms for measuring the accuracy with which information was conveyed to the Reader.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose the beginnings of a formal framework for modeling narrative \textit{qua} narrative. Our framework affords the ability to discuss key qualities of stories and their communication, including the flow of information from a Narrator to a Reader, the evolution of a Reader's story model over time, and Reader uncertainty. We demonstrate its applicability to computational narratology by giving explicit algorithms for measuring the accuracy with which information was conveyed to the Reader and two novel measurements of story coherence.

Related papers

• M-SENSE: Modeling Narrative Structure in Short Personal Narratives Using Protagonist's Mental Representations [14.64546899992196]
  We propose the task of automatically detecting prominent elements of the narrative structure by analyzing the role of characters' inferred mental state. We introduce a STORIES dataset of short personal narratives containing manual annotations of key elements of narrative structure, specifically climax and resolution. Our model is able to achieve significant improvements in the task of identifying climax and resolution.
  arXiv  Detail & Related papers  (2023-02-18T20:48:02Z)
• NECE: Narrative Event Chain Extraction Toolkit [64.89332212585404]
  We introduce NECE, an open-access, document-level toolkit that automatically extracts and aligns narrative events in the temporal order of their occurrence. We show the high quality of the NECE toolkit and demonstrate its downstream application in analyzing narrative bias regarding gender. We also openly discuss the shortcomings of the current approach, and potential of leveraging generative models in future works.
  arXiv  Detail & Related papers  (2022-08-17T04:30:58Z)
• Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework [68.1678127433077]
  We extend the Transformer model to dynamically conduct entity state updates and sentence realization for narrative generation. Experiments on two narrative datasets show that our model can generate more coherent and diverse narratives than strong baselines.
  arXiv  Detail & Related papers  (2022-08-08T09:02:19Z)
• SNaC: Coherence Error Detection for Narrative Summarization [73.48220043216087]
  We introduce SNaC, a narrative coherence evaluation framework rooted in fine-grained annotations for long summaries. We develop a taxonomy of coherence errors in generated narrative summaries and collect span-level annotations for 6.6k sentences across 150 book and movie screenplay summaries. Our work provides the first characterization of coherence errors generated by state-of-the-art summarization models and a protocol for eliciting coherence judgments from crowd annotators.
  arXiv  Detail & Related papers  (2022-05-19T16:01:47Z)
• Guiding Neural Story Generation with Reader Models [5.935317028008691]
  We introduce Story generation with Reader Models (StoRM), a framework in which a reader model is used to reason about the story should progress. Experiments show that our model produces significantly more coherent and on-topic stories, outperforming baselines in dimensions including plot plausibility and staying on topic.
  arXiv  Detail & Related papers  (2021-12-16T03:44:01Z)
• Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization [58.965859508695225]
  We propose an end-to-end neural model for dialogue summarization with two novel modules. The supporting utterance flow modeling helps to generate a coherent summary by smoothly shifting the focus from the former utterances to the later ones. The fact regularization encourages the generated summary to be factually consistent with the ground-truth summary during model training.
  arXiv  Detail & Related papers  (2021-08-03T03:09:25Z)
• Stylized Story Generation with Style-Guided Planning [38.791298336259146]
  We propose a new task, stylized story gen-eration, namely generating stories with speci-fied style given a leading context. Our model can controllably generateemo-tion-driven or event-driven stories based on the ROCStories dataset.
  arXiv  Detail & Related papers  (2021-05-18T15:55:38Z)
• 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)
• Narrative Interpolation for Generating and Understanding Stories [52.463747140762145]
  We propose a method for controlled narrative/story generation where we are able to guide the model to produce coherent narratives with user-specified target endings. The core of our method is an incrementally model based on GPT-2 which conditions on a previous sentence and a next sentence in a narrative and fills in the gap. We show that ending-guided generation results in narratives which are coherent, faithful to the given ending guide, and require less manual effort on the part of the human guide writer than past approaches.
  arXiv  Detail & Related papers  (2020-08-17T16:45:50Z)
• Tension Space Analysis for Emergent Narrative [0.1784936803975635]
  We present a novel approach to emergent narrative using the narratological theory of possible worlds. We demonstrate how the design of works in such a system can be understood through a formal means of analysis inspired by expressive range analysis. Finally, we propose a novel way through which content may be authored for the emergent narrative system using a sketch-based interface.
  arXiv  Detail & Related papers  (2020-04-22T19:26:09Z)
• Temporal Embeddings and Transformer Models for Narrative Text Understanding [72.88083067388155]
  We present two approaches to narrative text understanding for character relationship modelling. The temporal evolution of these relations is described by dynamic word embeddings, that are designed to learn semantic changes over time. A supervised learning approach based on the state-of-the-art transformer model BERT is used instead to detect static relations between characters.
  arXiv  Detail & Related papers  (2020-03-19T14:23:12Z)

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.