Text Article Summarization with Semantic Selection and Clustering
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Solution Overview
Problem
Existing text summarization models face challenges in identifying the right set of sentences for summarization and optimizing the number of sentences without losing context, leading to inefficiencies in time and cost.
Innovation Solution
A method and system that utilize keyword extraction, semantic scoring, contextual classification, and clustering to select and optimize sentences for summarization, using techniques like distance calculation, similarity algorithms, and word embeddings to ensure relevance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If many sentences are given as input for summarization, then the quality of the summary is improved, but the time and cost of summarization increases
Solution Approach 1:
The patent applies preliminary action by performing keyword extraction and semantic scoring on all sentences before summarization. The system pre-processes the text by identifying important keywords and calculating semantic scores for each sentence, then uses this pre-computed information to select the optimal set of sentences for summarization. This preliminary processing enables efficient sentence selection without requiring re-processing during the actual summarization task.
Solution Approach 2:
The patent extracts and removes irrelevant sentences from the input text by using keyword extraction algorithms and semantic scoring to identify only the most important sentences. The system extracts sentences that contain key keywords and have high semantic scores, filtering out redundant or less important sentences before feeding them to the summarization model. This extraction process reduces the input size while maintaining summary quality.
2Reliability
If many sentences are given as input for summarization, then the quality of the summary is improved, but the cost of summarization increases
Solution Approach 1:
The patent extracts and removes irrelevant sentences from the input text by using keyword extraction algorithms and semantic scoring to identify only the most important sentences. The system extracts sentences that contain key keywords and have high semantic scores, filtering out redundant or less important sentences before feeding them to the summarization model. This extraction process reduces the input size while maintaining summary quality.
Solution Approach 2:
The patent applies preliminary action by performing keyword extraction and semantic scoring on all sentences before summarization. The system pre-processes the text by identifying important keywords and calculating semantic scores for each sentence, then uses this pre-computed information to select the optimal set of sentences for summarization. This preliminary processing enables efficient sentence selection without requiring re-processing during the actual summarization task.
3Loss of time
If sentences are optimized to reduce the number of sentences, then the time and cost of summarization is reduced, but context may be lost
Solution Approach 1:
The patent implements feedback mechanisms where the semantic scoring and keyword extraction processes continuously refine the sentence selection. The system evaluates the semantic relevance of sentences based on extracted keywords and adjusts the selection to ensure sufficient context is retained. This feedback loop ensures that sentence optimization does not compromise the contextual integrity needed for accurate summarization.
Solution Approach 2:
The patent changes parameters such as semantic score thresholds and keyword weightings to optimize the balance between sentence reduction and context preservation. By adjusting these parameters, the system can control the number of sentences selected while maintaining adequate contextual information. Different parameter settings allow the system to adapt to various document types and summarization requirements.
Data Source
AI summary
A system and method for summarizing text articles of documents is disclosed. The method includes receiving a text article from a document. The text article may include a plurality of sentences. The method further includes extracting one or more keywords from the plurality of sentences; identifying a set of additional keywords corresponding to the one or more keywords; semantically scoring the plurality of sentences; ranking each of the plurality of sentences based on the semantical scoring; performing a contextual classification to select a set of sentences from the plurality of sentences based on one of the semantically scoring or the ranking; clustering each of the set of sentences based on one of the semantic scoring or embedding to generate a summarized text for each cluster; and generating a consolidated summarized text based on the clustering.