Text Summarization via Paragraph Vector Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing text summarization techniques face challenges in dynamically generating concise summaries from lengthy documents, particularly in updating data instances and ensuring validity across instances, making it difficult to retrieve relevant information efficiently.
Innovation Solution
A method and system that segregate input text into paragraphs, create paragraph-vectors, cluster them based on similarity with user-input tokens, and output relevant paragraphs or sentences, using techniques like Word2vec and GloVe models for vector representation and hierarchical or K-Means clustering to ensure accurate summarization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated text summarization techniques are used, then productivity is improved, but measurement precision deteriorates due to difficulty in ensuring data validity across instances
Solution Approach 1:
The patent implements feedback mechanisms where the summarization system continuously validates and exchanges data between instances, ensuring that each instance remains updated with data from other instances. This feedback loop maintains data validity and consistency across the distributed system while preserving automated summarization efficiency.
Solution Approach 2:
The patent introduces an intermediary validation layer that mediates data exchange between summarization instances. This intermediary ensures data validity by verifying and coordinating information flow between instances, resolving the contradiction between automated processing and data accuracy.
2Adaptability or versatility
If semantic-based dynamic summarization is implemented, then adaptability is improved, but device complexity increases due to multiple processing steps
Solution Approach 1:
The patent segments the complex summarization task into distinct functional modules: text reception, segmentation into paragraphs, vector creation, clustering, and output generation. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable while preserving dynamic adaptability to user queries.
Solution Approach 2:
The patent implements dynamic summarization where the system adapts its processing based on user input tokens. The clustering and selection of relevant paragraphs are dynamically adjusted according to the semantic similarity between user queries and text content, enabling versatility while organizing complexity through structured modular processing.
Data Source
AI summary
In an embodiment, a method of performing summarization of text is disclosed. The method may include receiving an input text including a plurality of paragraphs and a user-query including one or more tokens. The method may further include segregating the input text into the plurality of paragraphs, creating a plurality of paragraph-vectors representative of the plurality of paragraphs, and clustering the plurality of paragraph-vectors to generate one or more clusters of paragraph-vectors. The method may further include determining a relevant cluster of paragraph-vectors from the one or more clusters of paragraph-vectors, based on a degree of similarity of each cluster of paragraph-vectors with the user-query. The relevant cluster of paragraph-vectors is representative of a set of relevant paragraphs from the input text. The set of relevant paragraphs corresponding to the relevant cluster of paragraph-vectors may be outputted.