An Overview of the "Plan-And-Write" Framework for Automatic Storytelling
The paper "Plan-And-Write: Towards Better Automatic Storytelling" addresses the challenge of generating coherent narratives by proposing a hierarchical framework that enhances the process of automatic story creation. This framework is notable for its focus on open-domain story generation driven by given topics, expanding the applicability and reliability of automated storytelling systems.
At the core of the proposed approach is a plan-and-write framework that divides the storytelling process into two distinct stages: story planning and surface realization. The concept is inspired by earlier work on dialog and narrative planning, allowing for a systematic decomposition of story generation tasks. This is fundamental in improving both the diversity and coherence of narratives produced by the model.
Hierarchical Story Generation Framework
The paper highlights two specific methodologies within the plan-and-write framework: the dynamic schema and static schema. Both schemas rely on the generation of a storyline as an intermediary step, which serves to outline the plot structure of the story before the full narrative is written.
- Dynamic Schema: This approach generates the storyline incrementally alongside the story, planning the next plot point while simultaneously writing each sentence. The implementation involves a content-introducing generation model where the next keyword in the storyline aids in crafting the ensuing sentence of the narrative. This schema demonstrates a high degree of flexibility, encouraging the generation of narratives that can adapt fluidly to changes in plot direction as the story progresses.
- Static Schema: Conversely, the static schema establishes a complete storyline before any sentence crafting begins. This foresight potentially reinforces narrative coherence at the cost of flexibility. Statistical modeling with sequence-to-sequence frameworks is applied to generate these structured outlines from the title, subsequently informing story generation throughout.
Experimental Results and Evaluation
The empirical evidence presented in the paper indicates that storylines greatly enhance narrative quality by safeguarding thematic coherence and improving narrative diversity compared to baseline systems that do not utilize an explicit planning component. The paper reports significant reductions in repetition across generated stories, with metrics quantitatively supporting these claims. Human evaluations further corroborate these findings, consistently favoring the plan-and-write method over non-planning baselines in aspects such as coherence and overall preference.
The human annotator surmises an inclination towards coherent, interesting narratives, emphasizing that future frameworks should weigh these aspects more when refining automated storytelling models.
Implications and Future Directions
The implications of this work broaden potential applications in interactive story creation, virtual assistants, and educational technologies where dynamic narrative adaptations are valuable. By improving story generation technology, AI systems can engage users more effectively, maintaining interest and enhancing user experience.
Looking forward, the paper suggests exploring richer and more structured plot representations to build upon the existing framework. Furthermore, advancing inductive learning techniques to develop storylines without exhaustive human annotation is a promising avenue that could refine the autonomy and scalability of these systems.
In summary, the "Plan-and-Write" framework constitutes a notable advance in story generation by underscoring the importance of structured narrative planning and providing a clear methodology to enhance the coherence and diversity of machine-generated stories. This work lays a foundation for more sophisticated storytelling systems that could redefine automated content generation in rich and varied domains.