| Challenge: | Current storytelling systems focus more on generating stories with coherent plots regardless of the narration style. |
| Approach: | They propose a novel task, stylized story generation, that first plans stylized keywords and then generates the whole story with the guidance of the keywords. |
| Outcome: | The proposed model can generate emotion-driven or event-driven stories based on the ROCStories dataset . |
Similar Papers
Text-to-Text Automatic Story Generation: A Survey (2026.eacl-srw)
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| Challenge: | Automated story generation aims to produce coherent, engaging, and contextually consistent narratives with minimal or no human involvement . despite advances in large language models, maintaining narrative coherence, character consistency, storyline diversity, and plot controllability in generating stories is still challenging. |
| Approach: | They propose to develop new evaluation metrics and better data sets to support automatic story generation. |
| Outcome: | The proposed evaluation metrics and better datasets will improve narrative coherence and consistency and explore practical applications of story generation. |
Persona-Guided Planning for Controlling the Protagonist’s Persona in Story Generation (2022.naacl-main)
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| Challenge: | Existing methods to control the protagonist's persona in story generation are implicitly and sparsely embodied in stories, so we propose a planning-based generation model called ConPer to explicitly model the relationship between personas and events. |
| Approach: | They propose a model to control the protagonist's persona in story generation by predicting one target sentence and planning the plot as a sequence of keywords with the guidance of the predicted persona-related events and commonsense knowledge. |
| Outcome: | The proposed model outperforms state-of-the-art models for generating more coherent and persona-controllable stories. |
Strategies for Structuring Story Generation (P19-1)
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| Challenge: | Existing language models generate word by word, but fail to capture high-level interactions . a novel decomposition approach allows more abstract representations to be generated first . |
| Approach: | They propose models which abstract over actions and entities to create stories . they generate predicate-argument structure, then replace placeholders with context-sensitive names . |
| Outcome: | The proposed models improve diversity and coherence of events and entities in generated stories. |
Little Red Riding Hood Goes around the Globe: Crosslingual Story Planning and Generation with Large Language Models (2024.lrec-main)
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| Challenge: | Existing work has demonstrated the effectiveness of planning for story generation exclusively in a monolingual setting focusing primarily on English. |
| Approach: | They propose a task of crosslingual story generation with planning to leverage the creative and reasoning capabilities of large pretrained language models to generate stories in multiple languages. |
| Outcome: | The proposed task combines planning and planning in a monolingual setting and demonstrates that plans which structure stories into three acts lead to more coherent and interesting narratives while allowing to explicitly control their content and structure. |
Cue Me In: Content-Inducing Approaches to Interactive Story Generation (2020.aacl-main)
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| Challenge: | Existing methods for automatic story generation focus on one-shot generation, but we focus on interactive story generation. |
| Approach: | They propose two ways to incorporate user-provided cue phrases into automatic story generation. |
| Outcome: | The proposed approach produces more topically coherent and personalized stories than baseline methods. |
Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning (2022.findings-emnlp)
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| Challenge: | Existing methods to automate story generation focus on single-character stories and lack basiccommonsense reasoning. |
| Approach: | They propose a commonsense-inference Augmentedneural StoryTelling framework that introduces commonsensical reasoning into the story generation process. |
| Outcome: | The proposed method produces significantly more coherent, on-topic, enjoyable andfluent stories than existing models in both the single-character and two-character settings. |
Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)
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| Challenge: | Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization. |
| Approach: | They propose an end-to-end trained two-step text generation model that considers sentence-level content planners and language styles. |
| Outcome: | The proposed model outperforms competing models in three domains with diverse topics and varying language styles. |
Hierarchical Neural Story Generation (P18-1)
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| Challenge: | a hierarchical model that generates a premise and then conditions on it creates fluent text . a novel form of model fusion improves the relevance of the story to the prompt . |
| Approach: | They use a hierarchical model that first generates a premise, then transforms it into a text . they use fusion to improve relevance of the story to the prompt and add a gated mechanism to model context . |
| Outcome: | The proposed model improves on strong baselines on automated and human evaluations. |
A Survey on LLMs for Story Generation (2025.findings-emnlp)
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Maria Teleki, Vedangi Bengali, Xiangjue Dong, Sai Tejas Janjur, Haoran Liu, Tian Liu, Cong Wang, Ting Liu, Yin Zhang, Frank Shipman, James Caverlee
| Challenge: | Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. |
| Approach: | They propose a novel taxonomy of LLMs for story generation consisting of two major paradigms: independent story generation by an LLM, and author-assistance for story creation . |
| Outcome: | The proposed taxonomy compares existing work on the topic with those of novel author-assistance models. |
Stylized Text Generation: Approaches and Applications (2020.acl-tutorials)
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| Challenge: | Text generation has played an important role in various applications of natural language processing. |
| Approach: | They present different settings of stylized text generation and introduce machine learning methods to represent style. |
| Outcome: | This paper presents a comprehensive literature review on stylized text generation . it focuses on the challenges and future directions of stylized generation based on machine learning . |