Generative AI Summarization for Broadcast Content Constraints
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Solution Overview
Problem
Conventional Large Language Models (LLMs) for text summarization often produce overly verbose summaries, making them unsuitable for presentation formats like news banners or televised teases, and struggle to generate stylized outputs required for web-based or broadcast journalism, which demands concise and accurately formatted content within strict time limits.
Innovation Solution
A summarization system that utilizes user-provided parameters to guide a machine-learning model in generating summaries, allowing for specific prompts that adjust the length, style, and format of the output to meet the requirements of different presentation formats, such as narrative length, cadence, and presentation speed, enabling the creation of concise and coherent summaries that can be adapted for various distribution channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional LLMs are used for text summarization, then comprehensive coverage of source text is achieved, but the summary becomes overly verbose and unsuitable for broadcast formats
Solution Approach 1:
The patent applies parameter changes by adjusting the temperature parameter of the LLM to control randomness and creativity in generation, and by modifying system prompts to enforce brevity constraints. These parameter adjustments allow the model to produce summaries that are both comprehensive and appropriately concise for broadcast formats.
Solution Approach 2:
The system dynamically adjusts generation parameters and applies iterative refinement processes where summaries are generated, evaluated against constraints, and regenerated if necessary. This dynamic approach allows the system to adapt to varying length requirements while maintaining information completeness.
2Loss of information
If conventional LLMs generate text summaries, then semantic understanding is achieved, but the output lacks stylized formatting required for web-based or televised presentation
Solution Approach 1:
The patent segments the summarization task into multiple stages: initial generation phase focused on semantic understanding, followed by a formatting phase where the same or a secondary model applies stylized formatting rules. This segmentation allows each phase to optimize for its specific goal without compromising the other.
Solution Approach 2:
The system uses an intermediary processing layer that takes the semantically accurate summary from the LLM and transforms it into the desired stylized format. This intermediary layer acts as a mediator that preserves semantic meaning while applying presentation-specific formatting requirements.
3Manufacturing precision
If manual editing is used to format summaries for broadcast, then accuracy and style control are achieved, but production time and costs increase
Solution Approach 1:
The system implements self-service by enabling the LLM to automatically adjust its own output based on prompt instructions and parameter settings. The model can autonomously format its summaries to meet broadcast requirements without requiring manual intervention, thereby maintaining precision while dramatically increasing production speed.
Solution Approach 2:
The system incorporates feedback mechanisms where generated summaries are evaluated against desired format specifications, and adjustments are made automatically based on this feedback. This closed-loop approach ensures formatting accuracy is maintained while eliminating manual editing steps.
Data Source
AI summary
Aspects of the disclosed technology provide solutions for generating summaries of a source text based on user specified content parameters. In some aspects, a process of the disclosed technology can include steps for receiving a first source text, the source text having a first textual attribute, receiving a content parameter, providing the source text and the content parameter to a machine-learning (ML) model, and receiving, from the ML model, a proto text based on the source text and the content parameter, wherein the proto text has second textual attribute, and wherein the second textual attribute is different than the first textual attribute. Systems and machine-readable media are also provided.