Focus-Driven Graph Content Extraction With Biased TextRank
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Solution Overview
Problem
Existing NLP text summarization and keyword extraction methods, such as TextRank, are resource-intensive and lack contextual embeddings and topical focus, leading to inefficiencies and suboptimal performance.
Innovation Solution
Biased TextRank, an unsupervised graph-based algorithm, uses document representation models and similarity measures to capture meaning closeness between graph nodes and a target text, assigning random restart probabilities to favor nodes relevant to the focus, enabling improved summarization and keyword extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional TextRank algorithms are used for content extraction, then resource consumption is high, but extraction accuracy and contextual understanding are insufficient
Solution Approach 1:
The patent modifies the random restart probability parameter in the PageRank algorithm by introducing a bias term based on query-text similarity. This parameter change allows the algorithm to focus computational resources on graph nodes more relevant to the query, improving extraction accuracy while reducing overall resource consumption compared to uniform random restarts
Solution Approach 2:
The patent performs preliminary computation of graph node embeddings and query-text similarity scores before executing the main PageRank algorithm. This preliminary action pre-identifies relevant nodes and biases the random restart process, avoiding the need to process all nodes equally and thereby reducing resource consumption while maintaining high extraction accuracy
2Adaptability or versatility
If uniform random restart probabilities are assigned to all graph nodes, then the algorithm is simple to implement, but it lacks topical focus and contextual understanding
Solution Approach 1:
The patent applies local quality by assigning different random restart probabilities to different graph nodes based on their similarity to the query text. Instead of uniform treatment, nodes locally relevant to the query receive higher restart probabilities, enabling topical focus while maintaining the overall simplicity of the PageRank framework
Solution Approach 2:
The patent introduces query-text similarity scores as an intermediary mechanism between the query and the graph nodes. This intermediary biases the random restart process without fundamentally changing the PageRank algorithm structure, achieving topical focus with minimal increase in algorithmic complexity
3Loss of information
If document representation models and similarity measures are integrated into TextRank, then contextual understanding improves, but computational overhead increases
Solution Approach 1:
The patent applies partial action by computing graph node embeddings and similarity measures only for nodes that will be used as restart points in the PageRank algorithm, rather than computing all possible similarities. This selective computation reduces computational overhead while maintaining contextual understanding where it matters most
Solution Approach 2:
The patent performs preliminary computation of document representations and node embeddings before the main extraction process. By pre-computing these contextual features, the algorithm avoids redundant calculations during the iterative PageRank process, reducing computational overhead while preserving contextual understanding
Data Source
AI summary
Systems and methods for processing natural language text using a graph obtain a natural language text and a query text, and parse that the natural language text into the plurality of text units, associating each with a graph node, and removing information leak text units from the plurality of text units. Connecting relationship between at least two of the remaining set of the plurality of text units are determined and associated with a graph edge between graph nodes. Based on the probabilistic relations between each graph node and the query text, graph node restart probabilities are determined for one or more of the graph nodes. The graph nodes that can be ranked.