Graph-Based Multi-Sentence Compression for Content Cohesion
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Solution Overview
Problem
Content authors face productivity issues due to the time-consuming and error-prone process of manually curating and repurposing existing content, which often results in inconsistencies and redundancy, as they search for relevant information across various channels and platforms.
Innovation Solution
A system and method for generating new content by identifying and compressing relevant source content using a graph-based formulation and weighting system, optimizing for relevance and coherence based on an input snippet, and iteratively generating candidate content until all relevant information is covered, while minimizing redundancy and ensuring desired content length.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content authors manually curate and repurpose existing content, then they can ensure content relevance and quality, but the process becomes time-consuming and reduces productivity
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing content from multiple sources into a structured corpus before the author needs it. When an author inputs a snippet, the system has already organized the source material, enabling rapid retrieval and synthesis without manual curation at the moment of content creation.
Solution Approach 2:
The patent introduces an intermediary system that acts as a mediator between existing content sources and the author. This system automatically retrieves, synthesizes, and drafts content based on the author's snippet input, handling the time-consuming curation tasks while the author focuses on guidance and review.
2Loss of information
If content authors search for and analyze relevant information from multiple sources, then they can ensure comprehensive coverage, but the process becomes complex and error-prone
Solution Approach 1:
The system performs multiple functions within a single integrated platform: it searches across diverse content sources, retrieves relevant information, synthesizes it into coherent drafts, and manages the entire curation process. This multi-functional approach eliminates the need for authors to manually perform each step separately, reducing complexity while maintaining comprehensive information coverage.
Solution Approach 2:
The system incorporates feedback mechanisms where the generated draft is evaluated against the original snippet and source materials, allowing for iterative improvement. The author can also provide feedback to refine the output, ensuring comprehensive coverage while simplifying the overall process through automated adjustments.
3Loss of substance
If content authors manually remove duplicative information and ensure coverage, then they can minimize redundancy, but the process becomes tedious and time-intensive
Solution Approach 1:
The patent replaces the manual mechanical process of identifying and removing duplicative information with an automated computational system. The system uses algorithms to detect redundancy, synthesize unique information, and generate non-repetitive content drafts, eliminating the tedious manual work while maintaining low redundancy levels.
Solution Approach 2:
The system changes the parameters of content synthesis by using automated algorithms that measure and optimize for uniqueness and information density. By transforming the curation task into a computational optimization problem, the system minimizes redundancy without requiring manual intervention, significantly reducing preparation time.
Data Source
AI summary
Embodiments of the present invention provide systems, methods, and computer storage media directed to facilitating corpus-based content generation, in particular, using graph-based multi-sentence compression to generate a final content output. In one embodiment, pre-existing source content is identified and retrieved from a corpus. The source content is then parsed into sentence tokens, mapped and weighted. The sentence tokens are further parsed into word tokens and weighted. The mapped word tokens are then compressed into candidate sentences to be used in a final content. The final content is assembled using ranked candidate sentences, such that the final content is organized to reduce information redundancy and optimize content cohesion.