Multimodal Content Fragment Generation System
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Solution Overview
Problem
Current content generation systems are limited in generating coherent multimodal content and are not adaptable to different content needs, often relying on a single content modality for summarization and failing to provide customizable experiences.
Innovation Solution
The technique generates content fragments aligned to specific criteria by combining text, images, and other media types, using a fragment generator system that includes a content retrieval module, fragment generator module, and style adaptor module to create tailored content variants that meet various content delivery platform and scenario requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current content generation systems use single modality summarization, then the system complexity is low, but the content adaptability and user experience quality deteriorate
Solution Approach 1:
The system segments the content generation process into distinct modules: a content retrieval module that fetches source content, a fragment generator module that creates multiple content fragments from the source, and a style adaptor module that applies different styles to these fragments. This segmentation allows each module to specialize in specific tasks, improving overall content adaptability while managing system complexity through modular design.
Solution Approach 2:
The fragment generator system is designed as a universal platform that can handle multiple content modalities (text, images, video, audio) and generate content suitable for various delivery platforms and scenarios. The style adaptor module provides multi-functionality by applying different styles to the same content fragments, enabling the system to adapt to diverse content needs without requiring separate systems for each use case.
2Adaptability or versatility
If systems generate multiple content modalities using one type of content, then content variety increases, but the ability to meet different content needs deteriorates
Solution Approach 1:
The system performs preliminary action by generating multiple content fragments from the source content before delivering them to the user. The fragment generator module creates several candidate fragments that capture different aspects or perspectives of the source content, and the style adaptor module prepares various styled versions. This preliminary generation allows the system to have ready-to-use content variants that maintain coherence with the source while being adaptable to different needs.
Solution Approach 2:
The style adaptor module applies local quality by allowing different styles to be applied to different content fragments or even different parts of the same fragment. This enables selective customization where specific content elements can be adapted to meet particular content needs while preserving the overall coherence and accuracy of the source information.
Data Source
AI summary
Content fragments aligned to content criteria enable rich sets of multimodal content to be generated based on specified content criteria, such as content needs pertaining to various content delivery platforms and scenarios. For instance, the described techniques take a set of content (e.g., text, images, etc.) along with a specified content criteria (e.g., business/user need) and creates content fragment variants that are tailored to the content criteria with respect to both the information presented as well as the style of the content presented.