Topic Sentence Extraction via Machine Learning Importance Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for extracting topic sentences from webpages are inaccurate due to reliance on manually summarized extraction rules, which fail to effectively represent the importance of words and phrases in relation to query terms.
Innovation Solution
A method and apparatus using a pre-built machine learning model to determine word feature values and partial order values for candidate topic sentences, selecting target topic sentences based on importance and correlation with query terms, and ranking webpages accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually summarized extraction rules are used to extract topic sentences, then the extraction process is simple and fast, but the accuracy of topic sentence extraction is low
Solution Approach 1:
The patent transforms the extraction process from rule-based to model-based by changing the fundamental parameter of how topic sentences are identified. Instead of using fixed extraction rules, the system uses a machine learning model that calculates importance values based on word features and their relationship with query terms, dynamically determining which sentences are most relevant.
Solution Approach 2:
The patent replaces the mechanical rule-based extraction system with a machine learning-based system. The manual extraction rules are substituted with an automated model that learns from training data to identify topic sentences, using algorithms to calculate importance values rather than following predefined mechanical rules.
2Reliability
If manually summarized extraction rules are used, then the system is easy to implement, but it fails to effectively represent the importance of words and phrases in relation to query terms
Solution Approach 1:
The patent applies preliminary action by pre-building a machine learning model before the actual topic sentence extraction process. The model is trained in advance using training data that includes query terms and corresponding webpages, allowing it to learn the relationships between words, phrases, and query relevance beforehand. This pre-processing enables the system to effectively represent importance without computing complex relationships during the actual extraction phase.
Solution Approach 2:
The patent introduces an importance value calculation mechanism as an intermediary between the extraction rules and the topic sentences. Instead of directly applying rules to select sentences, the system first calculates importance values for each sentence based on word features and their relationship with query terms, then uses these values to determine the final topic sentences. This intermediary layer enables more nuanced and accurate representation of relevance.
3Ease of operation
If traditional extraction methods are used, then processing is faster, but user satisfaction with search results is reduced
Solution Approach 1:
The patent uses preliminary action by pre-training the machine learning model with extensive training data that reflects user preferences and satisfaction metrics. The model learns from historical data which topic sentences lead to better user satisfaction, embedding this knowledge in advance. During actual operation, the pre-trained model quickly evaluates sentences without needing to perform complex real-time analysis, thus maintaining fast processing while improving user satisfaction.
Data Source
AI summary
In various embodiments, a method and an apparatus for extracting topic sentences of webpages are provided. The method comprises: obtaining candidate webpages, and a pre-built machine learning model, each candidate webpage contains multiple preselected candidate topic sentences, and each candidate topic sentence includes several word segments; determining word feature values that indicate importance levels of the word segments in each candidate webpage respectively, and inputting the word feature values to the machine learning model to obtain an importance value for each word segment; for each candidate webpage, determining a partial order value for each candidate topic sentence according to the importance values of the word segments included in the candidate topic sentence; and for each candidate webpage, selecting one of the plurality of candidate topic sentences that is associated with a partial order value larger than a preset threshold value as a target topic sentence of the candidate webpage.