NGEP: A Graph-based Event Planning Framework for Story Generation
This work addresses the challenge of narrative coherence and control in automated story generation, which is incremental as it builds on prior event planning methods.
The authors tackled the problem of generating coherent and controllable event sequences for story generation by proposing NGEP, a graph-based neural framework that outperforms state-of-the-art event planning approaches in both event sequence generation and downstream story generation tasks.
To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i.e. storylines) to guide story generation. Such prior works mostly employ end-to-end neural generation models to predict event sequences for a story. However, such generation models struggle to guarantee the narrative coherence of separate events due to the hallucination problem, and additionally the generated event sequences are often hard to control due to the end-to-end nature of the models. To address these challenges, we propose NGEP, an novel event planning framework which generates an event sequence by performing inference on an automatically constructed event graph and enhances generalisation ability through a neural event advisor. We conduct a range of experiments on multiple criteria, and the results demonstrate that our graph-based neural framework outperforms the state-of-the-art (SOTA) event planning approaches, considering both the performance of event sequence generation and the effectiveness on the downstream task of story generation.