Challenge: a neural narrative generation system interacts with humans to generate stories . a recent resurgence of interest in collaborative storytelling has led to new approaches .
Approach: They propose a neural narrative generation system that interacts with humans to generate stories.
Outcome: The proposed system improves story quality and user engagement under time constraints.

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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.
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.
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.
STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)

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Challenge: Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience.
Approach: They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story.
Outcome: The proposed approach outperforms existing methods in 84.33% of the trials.
Content Planning for Neural Story Generation with Aristotelian Rescoring (2020.emnlp-main)

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Challenge: Current approaches to narrative composition are plagued by difficulty in mastering structure, will veer between topics, and lack long-range cohesion.
Approach: They propose a plot-generation language model and a set of rescoring models that implement an aspect of good story-writing as detailed in Aristotle's Poetics.
Outcome: The proposed system improves the quality of the narrative generated from the proposed model and improves its relevance to a given prompt and quality of stories written with our principled plot structure.
Harnessing the power of LLMs: Evaluating human-AI text co-creation through the lens of news headline generation (2023.findings-emnlp)

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Challenge: Recent advances in Large Language Models (LLMs) have shattered the ceiling of human-like text generation.
Approach: They compared human-AI interaction types in LLM-assisted news headline generation to determine whether humans can best leverage them for writing.
Outcome: The guiding and selecting model outputs added the most benefit with the lowest cost (in time and effort) Furthermore, AI assistance did not harm participants’ perception of control compared to freeform editing.
Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision) (2024.acl-tutorials)

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Challenge: In this tutorial, we focus on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria.
Approach: This tutorial focuses on text-to-text generation, a class of natural language generation tasks that takes a piece of text as input and generates a revision that is improved according to some specific criteria.
Outcome: This tutorial focuses on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and generates a revision that is improved according to some specificcriteria.
StoryCoder: Narrative Reformulation for Structured Reasoning in LLM Code Generation (2026.acl-long)

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Challenge: Existing approaches augment reasoning steps or inject specific structure into how models think, but leave scattered problem conditions unchanged.
Approach: They propose a narrative reformulation framework that transforms code generation questions into coherent natural language narratives.
Outcome: The proposed framework improves the performance of 11 code generation models on HumanEval, LiveCodeBench, and CodeForces.
NarrativePlay: Interactive Narrative Understanding (2024.eacl-demo)

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Challenge: Existing systems for interactive agents focus on specific capabilities in predetermined scenarios.
Approach: They propose a novel system that allows users to role-play a fictional character and interact with other characters in narratives in an immersive environment.
Outcome: The proposed system generates human-like responses guided by personality traits extracted from narratives.
Fiction-Writing Mode: An Effective Control for Human-Machine Collaborative Writing (2023.eacl-main)

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Challenge: Large-scale pre-trained language models (PLMs) have demonstrated an exceptional aptitude for generating text with an exceptional degree of fluency and structure.
Approach: They propose to integrate writing skills curricula into human-machine collaborative writing scenarios by adding writing modes as a control for text generation models.
Outcome: The proposed model can be used to generate narrative fiction with a high level of accuracy and similarity with the professionally written target story.

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