Hybrid Text Summarization for Large Multi-Context Corpora
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Solution Overview
Problem
Conventional automatic summarization techniques struggle to handle large volumes of text data with diverse contexts, leading to inefficiencies and unsatisfactory outputs due to resource constraints and token limits, particularly in handling thousands of support tickets with varying issues.
Innovation Solution
A hybrid summarization model combining abstractive and extractive methods, utilizing extractive techniques to identify salient sentences and abstractive models like LLMs to generate concise summaries, leveraging graph-based ranking models and transformer-based models for text embeddings and clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional automatic summarization techniques are used to process large volumes of text data from multiple contexts, then processing speed is improved, but summarization quality deteriorates due to inability to handle diverse contexts
Solution Approach 1:
The patent segments the text data processing by first clustering sentences into groups based on semantic similarity using sentence embeddings and clustering algorithms (e.g., K-means). This segmentation divides the large volume of diverse text into manageable context groups, allowing the summarization model to process each group separately while maintaining overall quality and speed.
2Manufacturing precision
If manual summarization is used to ensure high quality summaries, then summarization quality is improved, but time consumption and resource requirements worsen
Solution Approach 1:
The patent introduces an automatic summarization model as an intermediary between manual summarization and raw text data. This model, trained on diverse context data, automatically generates high-quality summaries by learning from clustered sentence groups, thereby eliminating the need for time-consuming manual summarization while maintaining quality standards.
3Adaptability or versatility
If conventional summarization methods process text from different contexts, then processing coverage is improved, but resource consumption worsens due to computational complexity
Solution Approach 1:
The patent performs preliminary actions by pre-processing the text data into sentence embeddings and clustering them into semantic groups before summarization. This preliminary organization reduces the computational complexity during the actual summarization phase, allowing the system to handle diverse contexts efficiently without excessive resource consumption.
4Productivity
If extractive summarization is used to quickly identify key sentences, then processing speed is improved, but information completeness deteriorates
Solution Approach 1:
The patent merges extractive and abstractive summarization approaches. It uses extractive techniques to identify and cluster important sentences based on similarity, then applies abstractive summarization to generate comprehensive summaries that maintain information completeness. This combination leverages the speed of extractive methods while preserving the information richness of abstractive methods.
Data Source
AI summary
Systems and methods implement a summarization model that includes a combination of abstractive and extractive summarization are provided. Extractive summarization is applied to identify the most salient sentences from a text corpus. Abstractive summarization is utilized to generate a topic summary based on filtered outputs from the extractive summarization.