Opinion Prevalence Scoring for Document Summarization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing opinion summarization systems fail to distinguish between common and rarely expressed opinions, and rely heavily on human-generated references for training, which is impractical for large-scale data sets.
Innovation Solution
A method that splits documents into sentences, ranks them by opinion prevalence, forms groups with similar embeddings, and uses a trained generalization encoder-decoder model to generate summaries, emphasizing common and informative opinions without requiring reference summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing opinion summarization systems are used, then summaries can be generated, but they fail to distinguish between common and rarely expressed opinions
Solution Approach 1:
The patent segments the summarization process into distinct stages: sentence extraction, prevalence scoring, clustering, and generalization. This segmentation allows the system to measure opinion prevalence independently and use this measurement to guide the summarization process, thereby distinguishing between common and rare opinions while generating accurate summaries.
Solution Approach 2:
The system performs preliminary action by calculating opinion prevalence scores for all sentences before the actual summarization process. This preliminary measurement of opinion prevalence allows the system to prioritize and select sentences based on their prevalence, ensuring that common opinions are appropriately represented while maintaining the ability to identify rare but important opinions.
2Measurement precision
If human-generated reference summaries are used for training, then model accuracy can be improved, but it becomes impractical for large-scale data sets
Solution Approach 1:
The system implements self-service by automatically generating opinion prevalence scores and using these scores to guide the summarization process without requiring external human-generated reference summaries for training. The model learns to identify and summarize prevalent opinions through self-supervised learning on the raw data, making the system scalable to large datasets while maintaining accuracy.
Solution Approach 2:
The patent changes the training parameter from requiring reference summaries to using automatically calculated opinion prevalence scores. This parameter change allows the system to scale to large datasets because the automatic scoring mechanism can process unlimited data without the bottleneck of human annotation, while still achieving high summary accuracy through the prevalence-based selection process.
3Loss of information
If all sentences are included in the summary, then completeness is improved, but the summary becomes too long and loses focus on prevalent opinions
Solution Approach 1:
The system applies partial action by selectively including only the most prevalent opinions in the final summary rather than all sentences. The opinion prevalence scoring mechanism identifies and prioritizes the most representative sentences, allowing the summary to be concise and focused on prevalent opinions while still capturing the essential information from the document collection.
Solution Approach 2:
The patent applies local quality by varying the level of detail included in the summary based on opinion prevalence. High-prevalence opinions receive more representation in the summary, while low-prevalence opinions are either omitted or given minimal coverage. This local differentiation in information density allows the summary to maintain appropriate length while preserving completeness of important opinions.
Data Source
AI summary
Methods and systems for document summarization include splitting documents into sentences and sorting the sentences by a metric that promotes review opinion prevalence from the documents to generate a ranked list of sentences. Groups of sentences with similar embeddings are formed and a trained generalization encoder-decoder model is applied to output a common generalization of the sentences in each group. Sentences are added to a summary from the generalizations corresponding to the sentences in the ranked list, in rank-order, until a target summary length has been reached. An action is performed responsive to the summary.