Keywords Extraction Technology for Few-Shot Learning in Customer Service

Abstract

Customer service primarily involves interaction with clients through phone calls. Precise keyword extraction from customer complaint texts facilitates the implementation of intelligent task assignment and efficient response systems. However, existing keyword extraction technologies perform sub-optimally in the customer service domain of telecommunications operators and require substantial manual word segmentation. Given the pronounced clustering of customer service data, this research introduces a synonym matching approach and a few-shot learning-based method tailored for extracting keywords in this sector. This enables model training with minimal labelled data and computational resources. Using a dataset generated from the transcription of customer service calls, the proposed model demonstrates a 24.94% improvement in accuracy compared to popular existing methods.