MLD-EA: Check and Complete Narrative Coherence by Introducing Emotions and Actions

TL;DR

This paper introduces MLD-EA, a model that uses large language models to detect and fill narrative gaps by ensuring emotional and logical coherence, thereby improving story understanding and generation.

Contribution

The paper presents the MLD-EA model that leverages LLMs to identify narrative gaps and generate coherent sentences, addressing the lack of focus on logical coherence in story generation.

Findings

MLD-EA improves narrative coherence and emotional consistency.

The model enhances story generation quality.

Experimental results validate the effectiveness of MLD-EA.

Abstract

Narrative understanding and story generation are critical challenges in natural language processing (NLP), with much of the existing research focused on summarization and question-answering tasks. While previous studies have explored predicting plot endings and generating extended narratives, they often neglect the logical coherence within stories, leaving a significant gap in the field. To address this, we introduce the Missing Logic Detector by Emotion and Action (MLD-EA) model, which leverages large language models (LLMs) to identify narrative gaps and generate coherent sentences that integrate seamlessly with the story's emotional and logical flow. The experimental results demonstrate that the MLD-EA model enhances narrative understanding and story generation, highlighting LLMs' potential as effective logic checkers in story writing with logical coherence and emotional consistency. This work fills a gap in NLP research and advances border goals of creating more sophisticated and reliable story-generation systems.