Word Generation Model Tuning Summaries to Target Characteristics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computer-based methods for summarizing text are ineffective in tuning summaries to target characteristics, leading to loss of coherence and inefficiency, as they typically involve post-processing adjustments that result in inaccurate and complex summaries.
Innovation Solution
A word generation model is used to directly generate summaries by encoding input text and a target characteristic token, allowing for the automatic tuning of summaries to specific characteristics such as length, voice, or linguistic properties, using a convolutional neural network-based sequence-to-sequence framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If post-processing tuning methods are used to adjust summaries to target characteristics, then summaries can be customized to specific audiences or topics, but the coherence of the original summary is lost and the process becomes computationally inefficient
Solution Approach 1:
The patent applies preliminary action by incorporating target characteristic tuning directly into the summary generation process rather than performing adjustments afterward. The sequence-to-sequence model with attention mechanisms is trained to generate summaries that are pre-tuned to target characteristics, eliminating the need for complex post-processing operations and preserving summary coherence from the outset.
Solution Approach 2:
The patent merges the summary generation and target characteristic tuning into a single integrated process. The attention mechanism combines information from both the source text and target characteristic embeddings simultaneously during generation, rather than treating them as separate post-processing steps, thereby reducing computational complexity while maintaining adaptability.
2Manufacturing precision
If post-processing tuning is applied to adjust summary characteristics, then specific linguistic properties can be achieved, but the accuracy and coherence of the summary deteriorate
Solution Approach 1:
The patent implements feedback mechanisms through the attention model that continuously monitors and adjusts the generation process based on target characteristic requirements. The attention mechanism provides real-time feedback about which source text elements to focus on when generating words that satisfy specific linguistic characteristics, maintaining both precision and coherence without requiring post-processing corrections.
Solution Approach 2:
The patent applies dynamics by making the attention weights and generation process adaptive rather than static. The model dynamically adjusts its focus on different parts of the source text based on the target characteristics being optimized, allowing it to maintain coherence while achieving precise linguistic properties through flexible, context-dependent adjustments during generation.
3Productivity
If traditional summarization methods are used without target tuning, then computational efficiency is maintained, but the summaries cannot be effectively tailored to specific audiences or characteristics
Solution Approach 1:
The patent achieves universality by designing a sequence-to-sequence model with attention mechanisms that can handle multiple target characteristics simultaneously. The same core architecture can be tuned to generate summaries for different audiences, topics, and linguistic requirements without requiring separate models or complex post-processing, maintaining computational efficiency while providing versatile adaptability through a single multi-functional system.
Data Source
AI summary
Systems and methods for generating a tuned summary using a word generation model. An example method includes receiving, at a decoder of the word generation model, a training data learned subspace representation of training data. The method also includes identifying tunable linguistic characteristics of the word generation model and training the decoder to output a training tuned summary of the training data learned subspace representation based on at least one of the tunable linguistic characteristics. The method further includes receiving an input text and a target characteristic token, and generating, by the trained decoder of the word generation model, each word of a tuned summary of the input text from a learned subspace representation and from feedback about preceding words of the tuned summary, wherein the tuned summary is tuned to target characteristics represented by the target characteristic token.