User-Centric Document Summarization via Word Group Scoring
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Solution Overview
Problem
Existing information communication systems, such as search engines and virtual personal assistants, often overwhelm users with excessive information, making it difficult to identify relevant content objects and their portions, leading to inefficiencies in content access and processing.
Innovation Solution
A system and method that generates a summary of content objects by parsing them into word groups, assigning scores based on user preferences derived from past activity data, and selecting relevant word groups using user models and summarization rules to create a concise summary tailored to user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If comprehensive information from content objects is communicated to users, then information completeness is improved, but information overload and processing difficulty increase
Solution Approach 1:
The patent segments content objects into discrete word groups and sentences that can be independently scored and selected. This segmentation allows the system to process large amounts of information by breaking them into manageable units, applying user preference weights to each segment, and selecting only the most relevant portions for communication to the user.
Solution Approach 2:
The patent extracts and communicates only the most relevant information from content objects based on user preferences. By calculating relevance scores for each word group and selecting only those above a threshold or among the top N scores, the system extracts essential information while filtering out redundant or less relevant content, thus preventing information overload.
2Adaptability or versatility
If user-specific customization is applied to content summarization, then user relevance is improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing user preference weights for various topics and features before actual content summarization is needed. This preliminary customization allows the system to quickly apply pre-determined user preferences to new content without performing complex real-time analysis, thereby reducing system complexity during operation while maintaining high user relevance.
Solution Approach 2:
The patent changes parameters by applying different user preference weights to different word groups based on their topic classifications. By dynamically adjusting the relevance score of each word group according to user-specific parameters (preferences for certain topics, authors, or features), the system achieves user-specific customization through parameter variation rather than structural complexity.
3Quantity of substance
If comprehensive content objects are presented to users, then information availability is improved, but user attention and processing time increase
Solution Approach 1:
The patent extracts and communicates only the most relevant information from content objects based on user preferences. By calculating relevance scores for each word group and selecting only those above a threshold or among the top N scores, the system extracts essential information while filtering out redundant or less relevant content, thus preventing information overload.
Solution Approach 2:
The patent applies local quality by treating different portions of content objects differently based on their relevance to user preferences. Instead of uniformly processing or presenting all content, the system assigns different weights and priorities to different word groups based on their topical relevance, ensuring that users receive high-quality, relevant information first while less relevant information is either summarized or omitted.
Data Source
AI summary
Disclosed techniques can generate content object summaries. Content of a content object can be parsed into a set of word groups. For each word group, at least one topic to which the word group pertains can be identified and it can be determined, via a user model, at least one weight of the plurality of weights corresponding to the topic(s). For each word group, a score can be determined for the word group based on the weight(s). A subset of the set of word groups can be selected based on the scores for the word group. A summary of the content object can be generated that includes the subset but that does not include one or more other word groups in the set of word groups that are not in the subset. At least part of the summary of the content object can be output.