Automated Content Summary Generation Using Taxonomy-Based Snippet Ranking
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Solution Overview
Problem
Researchers, authors, professors, and students face challenges in finding and identifying quality primary references related to their areas of interest due to the vast and diverse electronic content, with publicly available searchable content like Wikipedia not providing authoritative information.
Innovation Solution
A method and system for automatically generating content summaries by receiving a taxonomy for a concept, generating an annotated dataset from an unstructured text corpus, parsing it into a structured document object, extracting and scoring snippets and definitions based on features such as occurrence and similarity, and ranking them for relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If publicly available searchable content like Wikipedia is used, then accessibility and ease of search are improved, but information quality and authority deteriorate
Solution Approach 1:
The system introduces an intermediary layer between users and primary sources by automatically generating content summaries from authoritative texts. This mediator processes and presents information in an accessible format while maintaining the authority of the original sources, thus improving ease of search without sacrificing information quality.
2Loss of information
If comprehensive text corpus analysis is performed, then information completeness is improved, but processing power and time requirements increase
Solution Approach 1:
The system extracts only the most relevant snippets and content summaries from the comprehensive text corpus based on similarity scoring and ranking algorithms. By taking out only the essential information rather than processing the entire corpus, the system maintains information completeness while significantly reducing processing time and computational resources.
3Measurement precision
If manual review and curation of content is performed, then information accuracy is improved, but productivity and scalability deteriorate
Solution Approach 1:
The system implements self-service through automated algorithms that perform content analysis, snippet extraction, similarity scoring, and ranking without human intervention. The automated processes maintain information accuracy through structured methodologies while achieving high productivity and scalability, eliminating the need for manual review and curation.
4Measurement precision
If detailed feature analysis of each snippet is performed, then snippet relevance is improved, but computational complexity increases
Solution Approach 1:
The system segments the complexity by breaking down snippet analysis into distinct, manageable features such as term frequency, position weighting, and similarity metrics. Each feature is calculated independently and then combined through a scoring formula, which maintains high snippet relevance while reducing overall computational complexity through modular processing.
Data Source
AI summary
A method of automatically generating content summaries for topics includes receiving a taxonomy for a concept and a text corpus. The method further includes generating an annotated dataset having term annotations corresponding to the concept from the text corpus based on the taxonomy, parsing the annotated dataset into a custom generated document object having a structured layout, determining features for the term annotations, and extracting snippets from the custom generated document object, where each of the snippets corresponds to a section of the custom generated document object. The method further includes scoring the snippets based on the features such that each of the snippets corresponds to a score, filtering one or more snippets from the snippets when one or more snippet filtering conditions is met, ranking the snippets into an ordered list for the concept based on the score, and providing, to a user computing device, the ordered list.