Context Summary Generation for Message Familiarity
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Solution Overview
Problem
In online social networks and collaboration tools, users often send messages that are not fully understood by recipients without a shared history, leading to misunderstandings, unnecessary follow-up messages, and increased network traffic.
Innovation Solution
A method and system for content analysis and context summary generation that determines user familiarity with message content, generates a contextual summary based on message history, and presents it to the user, while also providing authors with warnings to edit their messages to avoid confusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If users send messages in online social networks without checking recipient familiarity, then communication speed is improved, but misunderstanding and network traffic increase
Solution Approach 1:
The system performs preliminary analysis of message content and recipient familiarity before the message is sent. It generates context summaries in advance and prepares warnings for potential misunderstandings, allowing users to take corrective action before communication occurs, thus preventing information loss while maintaining communication speed
Solution Approach 2:
The system provides feedback to users by generating warnings when messages may be misunderstood and by offering context summaries. This feedback loop allows users to adjust their messages or provide additional context, improving message understanding without significantly slowing down communication
2Loss of information
If context summaries are generated for all messages, then message understanding is improved, but system complexity increases
Solution Approach 1:
The system applies context summary generation selectively based on local conditions - specifically when analysis detects potential misunderstandings or when recipient familiarity is low. This localized application of the complex function reduces overall system complexity while maintaining message context where needed
Solution Approach 2:
The system changes the parameter of context provision from a static binary state (context provided/not provided) to a dynamic continuum based on message analysis. Context summaries are generated with varying levels of detail based on the detected need, optimizing the balance between information preservation and system complexity
3Measurement precision
If message history is analyzed for every new message, then user familiarity determination is improved, but processing time increases
Solution Approach 1:
The system performs partial message history analysis by focusing on the most relevant factors for familiarity determination. It analyzes message content, recipient history, and topic relevance selectively rather than examining every historical message, achieving sufficient precision while reducing processing time
Solution Approach 2:
The system extracts only the essential elements needed for familiarity determination from the message history, such as key topics, recipient communication patterns, and contextual relevance. By extracting and analyzing only these critical factors rather than the complete message history, it achieves accurate familiarity assessment with reduced processing time
Data Source
AI summary
The method, computer program product and computer system may include computing device which may collect application data from an application and archive the application data into a datastore. The computing device may generate a network graph based on the archived application data. The computing device may detect a new message, containing content on one or more topics, posted in the application by an author. The computing device may determine familiarity of an anticipated user with the content of the new message and associate the new message with a message history in the application based on the anticipated user. The computing device may generate a message content summary of the new message based on the message history and present message content summary to the anticipated user.