Encoder-Decoder Model with Attention Graph for Abstractive Summarization
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Solution Overview
Problem
Existing summarization technologies face challenges in efficiently summarizing linguistic interactions, conversations, and encounters, particularly in generating abstractive summaries that are both concise and informative while minimizing loss of original information.
Innovation Solution
The use of an encoder-decoder machine learning model in conjunction with an attention graph data object to generate abstractive summarizations. This approach encodes utterances into representations that capture internal meaning and holistic relationships, allowing for the decoding of concise and informative summaries that retain significant information from the original utterances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If extractive summarization is used, then information retention is maintained, but summary length and readability are reduced
Solution Approach 1:
The patent replaces traditional extractive summarization (mechanical selection of existing text) with an encoder-decoder neural network model that generates abstractive summaries through learned transformations. The model encodes input utterances into contextual representations and decodes them into condensed summaries, substituting mechanical text extraction with intelligent semantic transformation that achieves both compression and information retention.
Solution Approach 2:
The patent transforms the summarization task from extracting fixed text segments to generating variable-length abstractive summaries through learned parameter transformations. The encoder-decoder model dynamically adjusts representation parameters based on input content, enabling flexible summary generation that optimizes both length and information density according to the specific conversation context.
2Productivity
If abstractive summarization is generated using encoder-decoder model, then summary efficiency and readability improve, but computational complexity increases
Solution Approach 1:
The patent divides the complex summarization task into two distinct phases handled by separate model components: encoding (transforming input to contextual representations) and decoding (generating summaries from representations). This segmentation allows each component to be optimized independently and facilitates parallel processing, reducing overall computational complexity while maintaining abstractive summarization capabilities.
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary between the encoder and decoder, enabling selective focus on relevant input portions during summary generation. This intermediary component reduces computational burden by filtering out irrelevant information and directing processing resources to key elements, thereby improving efficiency without sacrificing model capability.
3Measurement precision
If party-wise relationships are modeled using attention graph, then semantic accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary encoding of all utterances into contextual representations before the decoding and summary generation phase. This preliminary action pre-processes the input data, organizing semantic relationships in advance, which reduces the computational workload during the actual summarization process and decreases overall processing time while maintaining semantic accuracy.
Solution Approach 2:
The patent employs a dynamic attention mechanism that adaptively adjusts weights based on the specific input conversation and contextual relationships. Rather than using fixed processing for all inputs, the model dynamically focuses computational resources on the most relevant party-wise relationships, improving semantic accuracy for each specific case while optimizing processing time by avoiding uniform heavy computation across all elements.
Data Source
AI summary
Various embodiments provide for summarization of an interaction, conversation, encounter, and/or the like in at least an abstractive manner. In one example embodiment, a method is provided. The method includes generating, using an encoder-decoder machine learning model, a party-agnostic representation data object for each utterance data object. The method further includes generating an attention graph data object to represent semantic and party-wise relationships between a plurality of utterance data objects. The method further includes modifying, using the attention graph data object, the party-agnostic representation data object for each utterance data object to form a party-wise representation data object for each utterance data object. The method further includes selecting a subset of party-wise representation data objects for each of a plurality of parties. The method further includes decoding, using the encoder-decoder machine learning model, the subset of party-wise representation data objects for each party to form abstractive summary data object(s).