Probabilistic Summary Generation with Damping Factor
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Solution Overview
Problem
Conventional digital content management systems generate summaries that are substantially similar, failing to provide diverse options for different variants, which limits their suitability for different computing contexts and user needs.
Innovation Solution
The technology modifies importance scores of text units to generate diverse summaries by probabilistically selecting text units based on importance scores, using a damping factor to reduce the probability of selecting similar units in subsequent summaries, and updates probability distributions to promote diversity in generated summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional extractive summarization is used to generate multiple summaries, then the summarization process is simple and fast, but the generated summaries are substantially similar to one another, lacking diversity
Solution Approach 1:
The system performs preliminary actions by generating importance scores for all text units before summary generation, and by pre-establishing multiple probability distributions with different weighting schemes. This allows diverse summaries to be generated efficiently without repeating complex computations for each summary variant.
Solution Approach 2:
The system dynamically adjusts the weighting of importance scores through different probability distributions to generate diverse summaries. By modifying the weighting parameters and sampling differently from the same underlying distribution, the system produces varied summaries while maintaining quality, resolving the contradiction between simplicity and diversity.
2Adaptability or versatility
If multiple diverse summaries are generated for different computing contexts, then adaptability to various user needs is improved, but storage and processing requirements increase
Solution Approach 1:
Instead of storing multiple complete summary versions, the system creates lightweight probability distribution representations that can be copied and sampled to generate diverse summaries on-demand. This reduces storage requirements while maintaining the ability to produce multiple context-appropriate summaries when needed.
3Adaptability or versatility
If importance scores are modified to reduce selection probability of similar text units, then summary diversity is improved, but the selection process becomes more complex
Solution Approach 1:
The system changes parameters by modifying the weighting of importance scores through different probability distribution configurations. Rather than fundamentally altering the selection mechanism, it adjusts numerical parameters (weights, sampling probabilities) to achieve diversity, keeping the overall process relatively simple while effective.
Data Source
AI summary
A method for generating summaries includes selecting a first subset of text units of a text composition to incorporate into a first summary of the text composition using a weighting of the text units that indicates for each text unit a relative importance of including the text unit in summaries of the text composition. The weighting of the text units is modified to reduce the relative importance of each text unit in the first subset based on the text unit having been selected for the first subset. A second subset of the text units is selected to incorporate into a second summary of the text composition using the modified weighting of the text units. At least one of the first summary and the second summary are provided to a user device.