Dialog Summarization Using Next Response Prediction
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Solution Overview
Problem
Current automated text summarization models primarily focus on documents and lack effectiveness in summarizing conversational exchanges, such as customer service chats, requiring human agents to manually create summaries, which is time-consuming and inefficient.
Innovation Solution
A system utilizing a trained next response prediction (NRP) machine learning model to determine the significance of each utterance in a two-party multi-turn dialog, assigning scores, and selecting relevant utterances for an extractive summarization, automating the summarization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated text summarization models are applied to conversational exchanges, then productivity is improved, but the models lack effectiveness in summarizing dialogs compared to documents
Solution Approach 1:
The patent changes the approach from traditional document-based summarization parameters to dialog-specific parameters by using next response prediction metrics. Instead of treating all text equally, the system evaluates each utterance based on its predictive importance for the next response, adapting the summarization criteria to the conversational context.
Solution Approach 2:
The patent replaces manual summarization (human mechanical process) with an automated ML-based system. The mechanical substitution involves using trained models to automatically identify and select important utterances, replacing the human agent's manual writing process while maintaining or improving quality through systematic evaluation of utterance importance.
2Reliability
If human agents manually create summaries of customer service chats, then summary quality can be maintained, but time consumption increases
Solution Approach 1:
The system enables self-service summarization where the dialog itself provides the information needed for summarization. By using next response prediction, the system automatically identifies which utterances are important without requiring human judgment, allowing the data to serve its own summarization needs and eliminating manual intervention.
Solution Approach 2:
The patent introduces an intermediary ML model that acts as a bridge between the raw dialog data and the final summary. This intermediary system processes the dialog through next response prediction to identify important utterances, serving as a mediator that transforms unstructured conversation into structured summaries without direct human involvement in the selection process.
3Ease of manufacture
If traditional summarization models are used for dialogs, then implementation is simple, but they fail to capture the unique characteristics of conversational data
Solution Approach 1:
The patent introduces dynamics into the summarization process by using next response prediction, which inherently captures the dynamic nature of conversation. Instead of static importance scoring, the system evaluates utterances based on their predictive value for future responses, adapting to the flow and context of the dialog dynamically.
Solution Approach 2:
The patent makes the summarization system universal by using next response prediction, a technique that can be applied to any dialog regardless of topic or domain. The same ML-based approach works across different conversational contexts, making the system adaptable to various dialog types while maintaining a unified implementation framework.
Data Source
AI summary
Summarization of customer service dialogs by: receiving, as input, a two-party multi-turn dialog; applying a trained next response prediction (NRP) machine learning model to the received dialog, to determine a level of significance of each utterance in the dialog with respect to performing an NRP task over the dialog; assigning a score to each of the utterances in the dialog, based, at least in part, on the determined level of significance; and selecting one or more of the utterances for inclusion in an extractive summarization of the dialog, based, at least in part, on the assigned scores.