Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908583267926016 |
|---|---|
| author | Wu, Yisha Zhao, Cen Mia Cao, Yuanpei Su, Xiaoqing Mehdad, Yashar Ji, Mindy Cheng, Claire Na |
| author_facet | Wu, Yisha Zhao, Cen Mia Cao, Yuanpei Su, Xiaoqing Mehdad, Yashar Ji, Mindy Cheng, Claire Na |
| contents | We introduce an incremental summarization system for customer support agents that intelligently determines when to generate concise bullet notes during conversations, reducing agents' context-switching effort and redundant review. Our approach combines a fine-tuned Mixtral-8x7B model for continuous note generation with a DeBERTa-based classifier to filter trivial content. Agent edits refine the online notes generation and regularly inform offline model retraining, closing the agent edits feedback loop. Deployed in production, our system achieved a 3% reduction in case handling time compared to bulk summarization (with reductions of up to 9% in highly complex cases), alongside high agent satisfaction ratings from surveys. These results demonstrate that incremental summarization with continuous feedback effectively enhances summary quality and agent productivity at scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06677 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback Wu, Yisha Zhao, Cen Mia Cao, Yuanpei Su, Xiaoqing Mehdad, Yashar Ji, Mindy Cheng, Claire Na Computation and Language Artificial Intelligence Machine Learning We introduce an incremental summarization system for customer support agents that intelligently determines when to generate concise bullet notes during conversations, reducing agents' context-switching effort and redundant review. Our approach combines a fine-tuned Mixtral-8x7B model for continuous note generation with a DeBERTa-based classifier to filter trivial content. Agent edits refine the online notes generation and regularly inform offline model retraining, closing the agent edits feedback loop. Deployed in production, our system achieved a 3% reduction in case handling time compared to bulk summarization (with reductions of up to 9% in highly complex cases), alongside high agent satisfaction ratings from surveys. These results demonstrate that incremental summarization with continuous feedback effectively enhances summary quality and agent productivity at scale. |
| title | Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.06677 |