CHIRON: Rich Character Representations in Long-Form Narratives

• URL: http://arxiv.org/abs/2406.10190v2
• Date: Wed, 26 Jun 2024 14:22:18 GMT
• Title: CHIRON: Rich Character Representations in Long-Form Narratives
• Authors: Alexander Gurung, Mirella Lapata,
• Abstract summary: We propose CHIRON, a new character sheet' based representation that organizes and filters textual information about characters. We validate CHIRON via the downstream task of masked-character prediction, where our experiments show CHIRON is better and more flexible than comparable summary-based baselines. metrics derived from CHIRON can be used to automatically infer character-centricity in stories, and that these metrics align with human judgments.
• Score: 98.273323001781
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Characters are integral to long-form narratives, but are poorly understood by existing story analysis and generation systems. While prior work has simplified characters via graph-based methods and brief character descriptions, we aim to better tackle the problem of representing complex characters by taking inspiration from advice given to professional writers. We propose CHIRON, a new `character sheet' based representation that organizes and filters textual information about characters. We construct CHIRON sheets in two steps: a Generation Module that prompts an LLM for character information via question-answering and a Validation Module that uses automated reasoning and a domain-specific entailment model to eliminate false facts about a character. We validate CHIRON via the downstream task of masked-character prediction, where our experiments show CHIRON is better and more flexible than comparable summary-based baselines. We also show that metrics derived from CHIRON can be used to automatically infer character-centricity in stories, and that these metrics align with human judgments.

Related papers

• BookWorm: A Dataset for Character Description and Analysis [59.186325346763184]
  We define two tasks: character description, which generates a brief factual profile, and character analysis, which offers an in-depth interpretation. We introduce the BookWorm dataset, pairing books from the Gutenberg Project with human-written descriptions and analyses. Our findings show that retrieval-based approaches outperform hierarchical ones in both tasks.
  arXiv  Detail & Related papers  (2024-10-14T10:55:58Z)
• 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)
• Evolving Storytelling: Benchmarks and Methods for New Character Customization with Diffusion Models [79.21968152209193]
  We introduce the NewEpisode benchmark to evaluate generative models' adaptability in generating new stories with fresh characters. We propose EpicEvo, a method that customizes a diffusion-based visual story generation model with a single story featuring the new characters seamlessly integrating them into established character dynamics.
  arXiv  Detail & Related papers  (2024-05-20T07:54:03Z)
• StoryGPT-V: Large Language Models as Consistent Story Visualizers [39.790319429455856]
  generative models have demonstrated impressive capabilities in generating realistic and visually pleasing images grounded on textual prompts. Yet, the emerging Large Language Model (LLM) showcases robust reasoning abilities to navigate through ambiguous references. We introduce textbfStoryGPT-V, which leverages the merits of the latent diffusion (LDM) and LLM to produce images with consistent and high-quality characters.
  arXiv  Detail & Related papers  (2023-12-04T18:14:29Z)
• Personality Understanding of Fictional Characters during Book Reading [81.68515671674301]
  We present the first labeled dataset PersoNet for this problem. Our novel annotation strategy involves annotating user notes from online reading apps as a proxy for the original books. Experiments and human studies indicate that our dataset construction is both efficient and accurate.
  arXiv  Detail & Related papers  (2023-05-17T12:19:11Z)
• Detecting and Grounding Important Characters in Visual Stories [18.870236356616907]
  We introduce the VIST-Character dataset, which provides rich character-centric annotations. Based on this dataset, we propose two new tasks: important character detection and character grounding in visual stories. We develop simple, unsupervised models based on distributional similarity and pre-trained vision-and-language models.
  arXiv  Detail & Related papers  (2023-03-30T18:24:06Z)
• "Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding [31.803481510886378]
  We present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
  arXiv  Detail & Related papers  (2021-09-12T06:12:55Z)
• 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)

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.