Context-Informed Communication Summarization System
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Solution Overview
Problem
Individuals face communication overload due to large volumes of electronic communications, necessitating summaries for ease of reference, but existing methods lack context-aware summarization and personalized delivery based on communication attributes and policies.
Innovation Solution
A context-informed summarization process that determines the context, summarization attributes, and recipient attributes of electronic communications, creating summaries based on these attributes and sending them according to defined policies, applicable to various communication types such as emails, IM chats, and documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If summaries are created for all communications, then information overload is reduced, but device complexity and processing requirements increase
Solution Approach 1:
The system changes parameters of the summarization process based on communication context, including summary length, level of detail, and recipient. By adjusting these parameters dynamically rather than applying a fixed summarization approach to all communications, the system reduces information overload while avoiding unnecessary complexity for simple communications.
Solution Approach 2:
The summarization system is segmented into multiple independent components: context determination module, summarization attribute determination module, policy determination module, and summary creation module. Each component handles specific aspects independently, allowing the system to manage complexity through modular architecture while providing comprehensive summarization services.
2Measurement precision
If context-aware summarization is implemented, then summarization quality improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary context determination and policy evaluation before actual summarization. By pre-identifying relevant context elements and applicable policies, the system avoids redundant processing during the summary generation phase, thereby improving accuracy without excessive time penalty.
Solution Approach 2:
The system applies partial context analysis based on communication type and importance. For high-priority communications, full context-aware analysis is performed, while for routine communications, a streamlined analysis is sufficient. This selective approach maintains accuracy where needed while reducing processing time for less critical messages.
3Ease of operation
If personalized summary delivery based on recipient attributes is implemented, then communication effectiveness improves, but system complexity increases
Solution Approach 1:
The system automatically determines recipient attributes and selects appropriate summary formats without manual intervention. By implementing self-service logic that automatically matches recipients with suitable summary types based on their profiles and preferences, the system improves communication effectiveness while avoiding the complexity of manual configuration.
Solution Approach 2:
The recipient attribute processing module serves multiple functions: identifying recipient preferences, determining appropriate summary length, selecting delivery timing, and choosing communication channels. This multi-functional approach consolidates what could be separate complex systems into a single versatile module.
4Loss of information
If policy-based filtering is applied to multiple communication types, then information relevance improves, but processing overhead increases
Solution Approach 1:
The policy application process is dynamic rather than static. The system adjusts the stringency and scope of policy filtering based on communication characteristics, recipient attributes, and contextual factors. This dynamic approach ensures high information relevance for important communications while reducing processing overhead for routine messages.
Solution Approach 2:
Policy evaluation is performed periodically at key stages: initial communication classification, mid-process relevance assessment, and final summary generation. This periodic rather than continuous evaluation maintains information relevance while avoiding constant processing overhead throughout the entire communication lifecycle.
Data Source
AI summary
A method and computer program product for context-informed summarization is described. A method may comprise determining, via a computing device, a context of a communication. The method may further comprise determining, via the computing device, a summarization attribute for the communication based upon, at least in part, the context of the communication. The method may also comprise creating a summary of the communication based upon, at least in part, the summarization attribute.