# Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling

arXiv: https://arxiv.org/abs/2012.07311 · HTML: https://mlsanity.com/paper/2012.07311/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2020.

## Built on by (most cited first, 23 of 23)

- [DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization](https://mlsanity.com/paper/2109.02492/) (2021)
- [A Survey on Dialogue Summarization: Recent Advances and New Frontiers](https://mlsanity.com/paper/2107.03175/) (2021)
- [CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization](https://mlsanity.com/paper/2108.13139/) (2021)
- [Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining](https://mlsanity.com/paper/2109.04080/) (2021)
- [TODSum: Task-Oriented Dialogue Summarization with State Tracking](https://mlsanity.com/paper/2110.12680/) (2021)
- [Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization](https://mlsanity.com/paper/2204.04362/) (2022)
- [Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions](https://mlsanity.com/paper/2205.13190/) (2022)
- [The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends](https://mlsanity.com/paper/2409.14195/) (2024)
- [NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization](https://mlsanity.com/paper/2505.24575/) (2025)
- [Baichuan2-Sum: Instruction Finetune Baichuan2-7B Model for Dialogue Summarization](https://mlsanity.com/paper/2401.15496/) (2024)
- [ORCHID: A Chinese Debate Corpus for Target-Independent Stance Detection and Argumentative Dialogue Summarization](https://mlsanity.com/paper/2410.13667/) (2024)
- [Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality](https://mlsanity.com/paper/2301.12376/) (2023)
- [Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions](https://mlsanity.com/paper/2210.09894/) (2022)
- [Meeting Summarization: A Survey of the State of the Art](https://mlsanity.com/paper/2212.08206/) (2022)
- [Learning towards Selective Data Augmentation for Dialogue Generation](https://mlsanity.com/paper/2303.09719/) (2023)
- [Team Hitachi @ AutoMin 2021: Reference-free Automatic Minuting Pipeline with Argument Structure Construction over Topic-based Summarization](https://mlsanity.com/paper/2112.02741/) (2021)
- [Towards Understanding Omission in Dialogue Summarization](https://mlsanity.com/paper/2211.07145/) (2022)
- [TokenVerse: Towards Unifying Speech and NLP Tasks via Transducer-based ASR](https://mlsanity.com/paper/2407.04444/) (2024)
- [Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarization](https://mlsanity.com/paper/2203.15590/) (2022)
- [LLM-Based Insight Extraction for Contact Center Analytics and Cost-Efficient Deployment](https://mlsanity.com/paper/2503.19090/) (2025)
- [Boosting Summarization with Normalizing Flows and Aggressive Training](https://mlsanity.com/paper/2311.00588/) (2023)
- [UniSLU: Unified Spoken Language Understanding from Heterogeneous Cross-Task Datasets](https://mlsanity.com/paper/2507.12951/) (2025)
- [TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation](https://mlsanity.com/paper/2508.19856/) (2025)

## Abstract (arXiv)

In a customer service system, dialogue summarization can boost service efficiency by automatically creating summaries for long spoken dialogues in which customers and agents try to address issues about specific topics. In this work, we focus on topic-oriented dialogue summarization, which generates highly abstractive summaries that preserve the main ideas from dialogues. In spoken dialogues, abundant dialogue noise and common semantics could obscure the underlying informative content, making the general topic modeling approaches difficult to apply. In addition, for customer service, role-specific information matters and is an indispensable part of a summary. To effectively perform topic modeling on dialogues and capture multi-role information, in this work we propose a novel topic-augmented two-stage dialogue summarizer (TDS) jointly with a saliency-aware neural topic model (SATM) for topic-oriented summarization of customer service dialogues. Comprehensive studies on a real-world Chinese customer service dataset demonstrated the superiority of our method against several strong baselines.