Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts

Goo, Chih-Wen; Chen, Yun-Nung

doi:10.48550/arxiv.1809.05715

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…However, abstractive dialog summarization was less explored due to the lack of a suitable benchmark. Recent work (Wang & Cardie, 2016;Goo & Chen, 2018;Pan et al, 2018) created abstractive dialog summary benchmarks with existing dialog corpus. Goo & Chen (2018) annotated topic descriptions in AMI meeting corpus as the summary.…”

Section: Related Workmentioning

confidence: 99%

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Abstractive Dialog Summarization with Semantic Scaffolds

Ye¹,

Zhou²

2019

Preprint

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The demand for abstractive dialog summary is growing in real-world applications. For example, customer service center or hospitals would like to summarize customer service interaction and doctor-patient interaction. However, few researchers explored abstractive summarization on dialogs due to the lack of suitable datasets. We propose an abstractive dialog summarization dataset based on MultiWOZ (Budzianowski et al., 2018). If we directly apply previous state-of-the-art document summarization methods on dialogs, there are two significant drawbacks: the informative entities such as restaurant names are difficult to preserve, and the contents from different dialog domains are sometimes mismatched. To address these two drawbacks, we propose Scaffold Pointer Network (SPNet) to utilize the existing annotation on speaker role, semantic slot and dialog domain. SPNet incorporates these semantic scaffolds for dialog summarization. Since ROUGE cannot capture the two drawbacks mentioned, we also propose a new evaluation metric that considers critical informative entities in the text. On MultiWOZ, our proposed SPNet outperforms state-of-the-art abstractive summarization methods on all the automatic and human evaluation metrics.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Recent work (Wang & Cardie, 2016;Goo & Chen, 2018;Pan et al, 2018) created abstractive dialog summary benchmarks with existing dialog corpus. Goo & Chen (2018) annotated topic descriptions in AMI meeting corpus as the summary. However, topics they defined are coarse, such as "industrial designer presentation".…”

Section: Related Workmentioning

confidence: 99%

Abstractive Dialog Summarization with Semantic Scaffolds

Ye¹,

Zhou²

2019

Preprint

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Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes

Zhang

Zaheer

et al. 2021

AAAI

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High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the first unsupervised abstractive dialogue summarization model for tete-a-tetes (SuTaT). Unlike standard text summarization, a dialogue summarization method should consider the multi-speaker scenario where the speakers have different roles, goals, and language styles. In a tete-a-tete, such as a customer-agent conversation, SuTaT aims to summarize for each speaker by modeling the customer utterances and the agent utterances separately while retaining their correlations. SuTaT consists of a conditional generative module and two unsupervised summarization modules. The conditional generative module contains two encoders and two decoders in a variational autoencoder framework where the dependencies between two latent spaces are captured. With the same encoders and decoders, two unsupervised summarization modules equipped with sentence-level self-attention mechanisms generate summaries without using any annotations. Experimental results show that SuTaT is superior on unsupervised dialogue summarization for both automatic and human evaluations, and is capable of dialogue classification and single-turn conversation generation.

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Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning

Wang

Zhang

Liu

et al. 2020

Preprint

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Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks where the training objectives are given naturally according to the nature of the utterance and the structure of the multi-role conversation. Meanwhile, in order to locate essential information for dialogue summarization/extraction, the pretraining process enables external knowledge integration. The proposed fine-tuned pretraining mechanism is comprehensively evaluated via three different dialogue datasets along with a number of downstream dialogue-mining tasks. Result shows that the proposed pretraining mechanism significantly contributes to all the downstream tasks without discrimination to different encoders.

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Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts

Cited by 3 publications

References 18 publications

Abstractive Dialog Summarization with Semantic Scaffolds

Abstractive Dialog Summarization with Semantic Scaffolds

Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes

Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning

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