A Bag of Tricks for Dialogue Summarization

Khalifa, Muhammad; Ballesteros, Miguel; McKeown, Kathleen

doi:10.18653/v1/2021.emnlp-main.631

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…Recent dialogue summarization models can be categorized into three types: 1) data augmentation methods (Feng et al, 2021;Khalifa et al, 2021), which attempt to construct more pseudo-data to train a better model; 2) topicbased models (Zou et al, 2021;Qi et al, 2021), which track the change of topic information in the dialogue to generate more focused summary; and 3) semantic structure-based models Zhang et al, 2021a;Lei et al, 2021;Zhao et al, 2021;, which employs semantic structures to enhance the summarization model.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality

Liang¹,

Wu²,

Cui³

et al. 2023

Preprint

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Dialogue summarization aims to condense a given dialogue into a simple and focused summary text. Typically, both the roles' viewpoints and conversational topics change in the dialogue stream. Thus how to effectively handle the shifting topics and select the most salient utterance becomes one of the major challenges of this task. In this paper, we propose a novel topic-aware Global-Local Centrality (GLC) model to help select the salient context from all sub-topics. The centralities are constructed at both the global and local levels. The global one aims to identify vital subtopics in the dialogue and the local one aims to select the most important context in each subtopic. Specifically, the GLC collects sub-topic based on the utterance representations. And each utterance is aligned with one sub-topic. Based on the sub-topics, the GLC calculates global-and local-level centralities. Finally, we combine the two to guide the model to capture both salient context and sub-topics when generating summaries. Experimental results show that our model outperforms strong baselines on three public dialogue summarization datasets: CSDS, MC, and SAMSUM. Further analysis demonstrates that our GLC can exactly identify vital contents from sub-topics. 1

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Section: Related Workmentioning

confidence: 99%

Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality

Liang¹,

Wu²,

Cui³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A solution based on contextual calibration was proposed for tasks such as text classification, fact retrieval or information extraction [9]. Dialogue summarization prompt-tuning techniques based on negation understanding and name substitution for dialogues with multiple participants are investigated in [10]. There have been investigated fine-tuning approaches which also prove that prompt-based tuning increases the language models performances [11] [12].…”

Section: A Related Workmentioning

confidence: 99%

Prompt scoring system for dialogue summarization using GPT-3

Prodan¹,

Pelican²

2022

Preprint

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<p>Recent results in language processing show that language models are capable of performing several natural language tasks without the need of supervised learning. A challenging task for pre-trained language models is dialogue summarization. One way of generating summaries is engineering prompt templates for few-shot training. However, a static approach of creating prompts leads to unreliable outcomes between different classes of dialogues. Focusing on the dialogues structure properties, we propose a scoring system to improve the few-shot training performances by building tuned prompts composed by the highest scored dialogue samples. Our evaluation based on ROUGE scores and human evaluation shows that there is improvement for the experiments where we made use of the score system. The positive results are validated by all the three large-scale datasets we used in testing. All experiments were performed within the framework of the GPT-3 API.</p>

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“…Dialogue Summarization The community has been seeing an increasing amount of interest in this subfield: from datasets (Zhu et al, 2021;Zhong et al, 2021;Fabbri et al, 2021; to models (Wu et al, 2021;Feng et al, 2021;Khalifa et al, 2021;Chen and Yang, 2020).…”

Section: Related Workmentioning

confidence: 99%

Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking

Shin¹,

Yu²,

Moon³

et al. 2022

Preprint

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Annotating task-oriented dialogues is notorious for the expensive and difficult data collection process. Few-shot dialogue state tracking (DST) is a realistic solution to this problem. In this paper, we hypothesize that dialogue summaries are essentially unstructured dialogue states; hence, we propose to reformulate dialogue state tracking as a dialogue summarization problem. To elaborate, we train a text-totext language model with synthetic templatebased dialogue summaries, generated by a set of rules. Then, the dialogue states can be recovered by inversely applying the summary generation rules. We empirically show that our method DS2 outperforms previous works on few-shot DST in MultiWoZ 2.0 and 2.1, in both cross-domain and multi-domain settings. Our method 1 also exhibits vast speedup during both training and inference as it can generate all states at once. Finally, based on our analysis, we discover that the naturalness of the summary templates plays a key role for successful training.

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A Bag of Tricks for Dialogue Summarization

Cited by 13 publications

References 12 publications

Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality

Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality

Prompt scoring system for dialogue summarization using GPT-3

Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking

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