Automated Narrative Story Generation System
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Solution Overview
Problem
Current technologies lack the ability to automatically generate narrative stories from domain-specific data in a comprehensible and compelling manner, failing to customize stories for specific audiences effectively.
Innovation Solution
A system and method that processes domain-related data to derive features, identify angles, filter and prioritize narrative elements, select and assemble relevant facts, and render stories in a customized format, using a network of processing devices to create engaging narratives across various domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual story creation is used, then story quality and audience engagement are improved, but time consumption and labor costs increase significantly
Solution Approach 1:
The story generation process is divided into distinct modules: data ingestion, feature derivation, angle proposal, filtering, fact selection, assembly, and rendering. Each module handles a specific aspect of story creation, enabling automated processing while maintaining quality through specialized handling of each stage.
Solution Approach 2:
Derived features and proposed angles act as intermediaries between raw domain data and final narrative stories. These intermediate representations enable the system to transform structured data into compelling narratives automatically, bridging the gap between data and story generation.
2Adaptability or versatility
If generic story templates are used, then generation speed is improved, but audience customization and engagement decrease
Solution Approach 1:
The system pre-computes derived features from domain data and generates multiple proposed angles before final story assembly. This preliminary processing enables rapid customization for different audiences without requiring complex real-time processing during story generation.
Solution Approach 2:
The system dynamically selects and adjusts story angles, facts, and narrative elements based on the target audience and domain context. This dynamic adaptation allows the same underlying data to generate customized stories for different audiences without manual intervention.
3Measurement precision
If comprehensive data analysis is performed, then story accuracy and relevance are improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the most relevant features and facts from comprehensive domain data through filtering mechanisms. By taking out only the essential elements needed for story generation rather than processing all available data, the system maintains accuracy while reducing processing time.
Solution Approach 2:
The system transforms raw domain data into derived features with specific parameters and characteristics that are optimized for story generation. This parameter transformation enables efficient processing while preserving the accuracy and relevance of the underlying data.
Data Source
AI summary
A system and method for automatically generating a narrative story receives data and information pertaining to a domain event. The received data and information and/or one or more derived features are then used to identify a plurality of angles for the narrative story. The plurality of angles is then filtered, for example through use of parameters that specify a focus for the narrative story, length of the narrative story, etc. Points associated with the filtered plurality of angles are then assembled and the narrative story is rendered using the filtered plurality of angles and the assembled points.