Document Content Reordering by Title Alignment Scoring
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Solution Overview
Problem
Existing document presentation technologies fail to accurately align content with titles, leading to reader confusion and inefficiency in browsing and reading electronic articles, as titles often do not accurately represent the article's content.
Innovation Solution
A computer-implemented method that analyzes document portions, determines their concepts, compares these concepts with the title, generates propensity scores, and reorders the content based on alignment with the title, using natural language understanding, deep learning, and graph theory to improve content alignment and presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional document presentation is used, then the document structure is simple and easy to implement, but the alignment between title and content is poor leading to reader confusion
Solution Approach 1:
The document is divided into multiple portions (paragraphs, sections) that are individually analyzed and scored for alignment with the title. Each segment receives a propensity score based on its conceptual content, allowing fine-grained alignment assessment and selective reordering without requiring complete document restructuring.
Solution Approach 2:
A computing device with specialized algorithms acts as an intermediary between the title and document content. The system uses natural language processing and concept extraction as intermediate steps to bridge the gap between title semantics and content portions, enabling accurate alignment determination without direct complex comparison.
2Productivity
If content is reordered based on alignment scoring, then reader engagement and navigation efficiency improve, but processing time and computational resources increase
Solution Approach 1:
The system performs alignment scoring and propensity score generation as preliminary actions before the reader actually navigates the document. By pre-computing which portions align best with the title and their corresponding scores, the system enables rapid reader navigation without requiring real-time computational analysis during reading.
Solution Approach 2:
The document structure is made dynamic through conditional reordering based on propensity scores. The system can adapt the document presentation dynamically - reordering portions for users who need alignment verification, while maintaining original structure for standard reading, thus optimizing between processing time and navigation efficiency based on user needs.
3Measurement precision
If automatic concept determination and scoring is implemented, then content alignment accuracy improves, but the complexity of analyzing and processing document portions increases
Solution Approach 1:
Manual concept analysis and alignment assessment is replaced with automated computing algorithms. The system uses natural language processing, concept extraction, and computational scoring mechanisms to automatically determine alignment without human intervention, achieving precise measurement while reducing operational complexity through automation.
Solution Approach 2:
The system transforms qualitative concept alignment assessment into quantitative propensity scores. By changing the measurement parameter from subjective alignment judgment to objective numerical scoring based on concept similarity and alignment metrics, the system achieves precise, reproducible measurement while simplifying the analysis process through standardized computational parameters.
Data Source
AI summary
A method including: analyzing, by a computing device, a plurality of portions of a document; determining, by the computing device and based on the analyzing, a concept of each of the portions of the document; comparing, by the computing device, a title of the document with the concept of each of the portions of the document; determining, by the computing device and based on the comparing, an alignment of the concept of each of the portions of the document with the title; generating, by the computing device and based on the alignment, a propensity score for each of the portions of the document; and reordering, by the computing device and based on the propensity scores, the portions of the document from most aligned with the title to least aligned with the title.