Email Thread Summarization via Weighted Content Extraction
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Solution Overview
Problem
Reviewing long email threads is time-consuming and inefficient, especially when working across time zones, as it requires navigating numerous emails to identify key information and consensus within the thread.
Innovation Solution
An automated system analyzes email threads by assigning weights to emails based on their position in the thread, word weights based on frequency and importance, and intersection scores for common words, generating a compact content summary that highlights key sentences and hyperlinks to relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If email threads are organized by conversation with all messages displayed, then complete information is preserved, but time to review the thread increases significantly
Solution Approach 1:
The email thread is segmented into two views: a condensed summary view that shows only key sentences extracted from each email, and a detailed view that displays complete emails on demand. The summary view segments the information hierarchy by email message, extracting only the most relevant sentences from each message to create a manageable overview.
Solution Approach 2:
Key sentences are extracted from each email message to create the condensed summary. The system identifies and extracts the most important sentences from each email in the thread, removing redundant information while preserving the essential meaning and context of each message.
2Loss of information
If all email messages in a thread are displayed in chronological order, then complete context is available, but navigation complexity increases
Solution Approach 1:
The thread is segmented into discrete email message units, each represented by its key sentences in the summary view. Users can navigate between segmented units (individual emails) rather than scrolling through continuous text, making navigation more manageable while preserving context through the structured presentation.
Solution Approach 2:
The system adds a dimensional transformation by converting the linear chronological sequence into a hierarchical structure with a summary layer and detail layer. The summary view provides a high-level overview dimension, while individual email details remain accessible on demand, creating multiple dimensions of information access.
3Productivity
If automated analysis is applied to assign weights to emails and sentences, then key information is highlighted, but system complexity increases
Solution Approach 1:
The system performs self-service automated analysis by automatically analyzing email content, assigning weights to emails based on their position and importance in the thread, and identifying key sentences without requiring manual intervention. The weighting algorithm automatically determines which emails and sentences deserve prominence in the summary.
Solution Approach 2:
The system applies parameter changes by assigning numerical weights to emails and sentences based on various factors such as position in thread, sender importance, and content significance. These parameter changes automatically prioritize information in the summary, with higher-weighted items appearing more prominently.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for enhancing an email application to automatically analyze an email thread and generate a compact content summary. The content summary is based on relative content contributions provided by the constituent email messages in the email thread. The content summary may be presented in a special window without disturbing or modifying the email thread or its constituent email messages. The distinctive content summary disclosed herein comprises certain sentences that are automatically gleaned from the email thread, analyzed relative to other sentences, and presented in a chronological sequence so that the user can quickly determine what the email thread is about and/or the current status of the conversation. The content summary is based on email weights, word weights, and intersecting sentence pairs.