Neutral Point of View Content Generation via Polarity Balancing
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Solution Overview
Problem
Existing content generation systems often reinforce popular or user-specific viewpoints, limiting access to factual content with neutral perspectives, as they prioritize popularity and relevance over diversity of opinions.
Innovation Solution
A method that constructs a concept tree from natural language text documents, scores polarity using a trained model, adds documents to achieve a neutral polarity range, filters sentences based on factuality, and generates new content using a transformer deep learning narration model to produce neutral point of view content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content management systems prioritize popularity and relevance when ranking content, then user engagement and content accessibility are improved, but content neutrality and perspective diversity deteriorate
Solution Approach 1:
The system segments content into multiple polarity groups (positive, negative, neutral) and organizes them separately in the concept tree structure. This allows the system to maintain and present diverse perspectives by selectively retrieving content from different polarity segments rather than mixing them together in a single ranked list.
Solution Approach 2:
The system changes the parameter of content representation by introducing polarity scores and concept tree levels. Instead of solely ranking by popularity or relevance, content is evaluated and organized based on polarity parameters, enabling the system to balance popularity with perspective diversity when generating neutral content.
2Ease of operation
If content generation systems reinforce popular viewpoints, then user engagement is improved, but factual neutrality and balanced perspective deteriorate
Solution Approach 1:
The system applies counterweight by introducing content from opposing polarities to balance out dominant viewpoints. When generating neutral content, the system deliberately incorporates sentences from both positive and negative polarity groups, preventing any single perspective from dominating and ensuring factual neutrality.
Solution Approach 2:
The concept tree structure serves as an intermediary mechanism that mediates between popular viewpoints and neutral content generation. The system uses the concept tree to identify and select content from multiple polarity groups, acting as a mediator that balances engagement with neutrality.
3Adaptability or versatility
If machines and users influence each other in content discovery, then personalized content delivery is improved, but access to diverse perspectives outside user's viewpoint deteriorates
Solution Approach 1:
Instead of allowing user preferences to dictate content selection (traditional approach), the system inverts the approach by using polarity-balanced content selection to influence user exposure. The system deliberately presents content from polarities different from what users might typically encounter, expanding their perspective while maintaining personalized delivery through the concept tree structure.
Data Source
AI summary
From a set of natural language text documents, a concept tree is constructed. For a node in the concept tree a polarity of the subset represented by the node is scored. A second set of natural language text documents is added to the subset, the adding resulting in a modified subset of natural language text documents having a polarity score within a predefined neutral polarity score range. From the modified subset, a bin of sentences is selected according to a sentence selection parameter, a sentence in the bin of sentences being extracted from a selected document in the modified subset. A sentence having a factuality score below a threshold factuality score is removed from the bin of sentences. From the filtered bin of sentences a new natural language text document corresponding to the filtered bin of sentences is generated using a transformer deep learning narration generation model.