Comment Summarization Using Semantic Clusters for Real-Time Updates
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Solution Overview
Problem
Existing comment section summarization techniques using language models (LMs) are computationally expensive and often fail to provide globally relevant summaries in real or near real time, especially when new comments are added, leading to outdated summaries.
Innovation Solution
A computing system analyzes new or edited comments to determine if they trigger a summary update by assigning them to semantic clusters and using a machine learning model to generate updated summaries based on limited representations of the comment section, reducing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a language model is used to generate summaries of all comments in the comment section, then the summaries are globally relevant and comprehensive, but the computational resource usage is high and the summaries cannot be generated in real or near real time
Solution Approach 1:
The patent segments the comment section into multiple semantic clusters based on topic or theme. Instead of processing all comments as a single large set, the system divides them into smaller, manageable clusters that can be processed independently and more efficiently by the language model.
Solution Approach 2:
The patent applies partial action by selectively updating only those cluster summaries that are affected by new or edited comments, rather than regenerating summaries for all clusters. This reduces computational overhead while maintaining accuracy for relevant portions of the comment section.
2Reliability
If the language model processes all comments to generate summaries, then the summaries represent all comments accurately, but the computational expense is too high for real-time updates
Solution Approach 1:
The comment section is divided into semantic clusters, allowing the system to process only relevant subsets of comments for each summary. This segmentation reduces the total computational load while maintaining summary reliability for each specific topic area.
Solution Approach 2:
The system pre-processes comments to assign them to semantic clusters before summary generation. This preliminary organization enables efficient retrieval and processing of only the comments relevant to each cluster, reducing computational expenses during summary generation.
3Loss of time
If summaries are updated frequently to reflect new comments, then the summaries remain current and relevant, but the computational overhead increases significantly
Solution Approach 1:
The system monitors changes in the comment section and uses feedback mechanisms to determine when summary updates are necessary. By tracking new comments and evaluating their impact on existing clusters, the system updates summaries only when meaningful changes occur, balancing currency with efficiency.
Solution Approach 2:
Instead of continuous updates, the system employs periodic action by updating summaries at intervals triggered by specific events (such as adding a threshold number of new comments or detecting significant changes). This reduces computational overhead while keeping summaries sufficiently current.
Data Source
AI summary
An example computing system includes one or more processors; and one or more storage devices that store instructions. The instructions, when executed by the one or more processors, may cause the one or more processors to: obtain a comment from a comment datastore; determine a respective semantic distance between the comment and each semantic cluster from a set of semantic clusters; determine whether the respective semantic distance indicating a greatest semantic similarity between the comment and a semantic cluster from the set of semantic clusters satisfies a threshold; responsive to a determination that the respective semantic distance satisfies the threshold, update a summary by at least applying a machine learning model to the comment, wherein the machine learning model is a language model; and store the summary to a datastore.