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

• URL: http://arxiv.org/abs/2012.07311v1
• Date: Mon, 14 Dec 2020 07:50:25 GMT
• Title: Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling
• Authors: Yicheng Zou, Lujun Zhao, Yangyang Kang, Jun Lin, Minlong Peng, Zhuoren Jiang, Changlong Sun, Qi Zhang, Xuanjing Huang, Xiaozhong Liu
• Abstract summary: In a customer service system, dialogue summarization can boost service efficiency by creating summaries for long spoken dialogues. In this work, we focus on topic-oriented dialogue summarization, which generates highly abstractive summaries. 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.
• Score: 61.67321200994117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Long Dialog Summarization: An Analysis [28.223798877781054]
  This work emphasizes the significance of creating coherent and contextually rich summaries for effective communication in various applications. We explore current state-of-the-art approaches for long dialog summarization in different domains and benchmark metrics based evaluations show that one single model does not perform well across various areas for distinct summarization tasks.
  arXiv  Detail & Related papers  (2024-02-26T19:35:45Z)
• Multi-turn Dialogue Comprehension from a Topic-aware Perspective [70.37126956655985]
  This paper proposes to model multi-turn dialogues from a topic-aware perspective. We use a dialogue segmentation algorithm to split a dialogue passage into topic-concentrated fragments in an unsupervised way. We also present a novel model, Topic-Aware Dual-Attention Matching (TADAM) Network, which takes topic segments as processing elements.
  arXiv  Detail & Related papers  (2023-09-18T11:03:55Z)
• Multi-Granularity Prompts for Topic Shift Detection in Dialogue [13.739991183173494]
  The goal of dialogue topic shift detection is to identify whether the current topic in a conversation has changed or needs to change. Previous work focused on detecting topic shifts using pre-trained models to encode the utterance. We take a prompt-based approach to fully extract topic information from dialogues at multiple-granularity, i.e., label, turn, and topic.
  arXiv  Detail & Related papers  (2023-05-23T12:35:49Z)
• STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension [42.57581945778631]
  Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing. We propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization. We show that our STRUDEL dialogue comprehension model can significantly improve the dialogue comprehension performance of transformer encoder language models.
  arXiv  Detail & Related papers  (2022-12-24T04:39:54Z)
• TODSum: Task-Oriented Dialogue Summarization with State Tracking [16.87549093925514]
  We introduce a large-scale public Task-Oriented Dialogue Summarization dataset, TODSum. Compared to existing work, TODSum suffers from severe scattered information issues and requires strict factual consistency. We propose a state-aware structured dialogue summarization model to integrate dialogue state information and dialogue history.
  arXiv  Detail & Related papers  (2021-10-25T06:53:11Z)
• CSDS: A Fine-grained Chinese Dataset for Customer Service Dialogue Summarization [44.21084429627218]
  We introduce a novel Chinese dataset for Customer Service Dialogue Summarization (CSDS) CSDS improves the abstractive summaries in two aspects: (1) In addition to the overall summary for the whole dialogue, role-oriented summaries are also provided to acquire different speakers' viewpoints. We compare various summarization methods on CSDS, and experiment results show that existing methods are prone to generate redundant and incoherent summaries.
  arXiv  Detail & Related papers  (2021-08-30T11:56:58Z)
• Advances in Multi-turn Dialogue Comprehension: A Survey [51.215629336320305]
  We review the previous methods from the perspective of dialogue modeling. We discuss three typical patterns of dialogue modeling that are widely-used in dialogue comprehension tasks.
  arXiv  Detail & Related papers  (2021-03-04T15:50:17Z)
• Topic-Aware Multi-turn Dialogue Modeling [91.52820664879432]
  This paper presents a novel solution for multi-turn dialogue modeling, which segments and extracts topic-aware utterances in an unsupervised way. Our topic-aware modeling is implemented by a newly proposed unsupervised topic-aware segmentation algorithm and Topic-Aware Dual-attention Matching (TADAM) Network.
  arXiv  Detail & Related papers  (2020-09-26T08:43:06Z)
• Recent Advances and Challenges in Task-oriented Dialog System [63.82055978899631]
  Task-oriented dialog systems are attracting more and more attention in academic and industrial communities. We discuss three critical topics for task-oriented dialog systems: (1) improving data efficiency to facilitate dialog modeling in low-resource settings, (2) modeling multi-turn dynamics for dialog policy learning, and (3) integrating domain knowledge into the dialog model.
  arXiv  Detail & Related papers  (2020-03-17T01:34:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.