| Challenge: | Using a pretrained sequence-to-sequence language model, we explore speaker name substitution, negation scope highlighting, multi-task learning with relevant tasks, and pretraining on in-domain data. |
| Approach: | They propose a pretrained sequence-to-sequence language model that can handle different parts of dialogue belonging to multiple speakers and combine them to produce a coherent monologue summary. |
| Outcome: | The proposed techniques outperform baseline models on a dialogue summarization dataset. |
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| Challenge: | Abstractive dialogue summarization aims to convert long dialogue content into its short form where the salient information is preserved while the redundant pieces are ignored. |
| Approach: | They propose to have the model perceive the redundant parts of an input dialogue history during the training phase. |
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Post-Training Dialogue Summarization using Pseudo-Paraphrasing (2022.findings-naacl)
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| Challenge: | Existing approaches to dialogue summarization use dialogue-specific features that require additional knowledge to recognize or make the models harder to tune. |
| Approach: | They propose to post-train pretrained language models to rephrase from dialogue to narratives and fine-tune them as usual. |
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Narrate Dialogues for Better Summarization (2022.findings-emnlp)
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| Challenge: | Recent work on dialogue summarization models focuses on generating concise summaries for multi-party dialogues. |
| Approach: | They propose several ways to convert dialogue into a third-person narrative style . they propose to use narration as a valuable annotation for LLMs . |
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An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next (2021.findings-emnlp)
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Yusen Zhang, Ansong Ni, Tao Yu, Rui Zhang, Chenguang Zhu, Budhaditya Deb, Asli Celikyilmaz, Ahmed Hassan Awadallah, Dragomir Radev
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Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning (2022.coling-1)
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| Challenge: | Existing summarization systems based on pre-trained models cannot recognize the unique format of the speaker-utterance pair well in the dialogue. |
| Approach: | They propose three speaker-aware supervised contrastive learning tasks to solve the speaker identification problem in dialogue summarization task. |
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Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining (2021.emnlp-main)
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| Challenge: | Existing methods for low-resource dialogue summarization neglect the difference between dialogues and conventional articles. |
| Approach: | They propose a multi-source pretraining paradigm to leverage external summary data . they exploit large-scale in-domain non-summary data to separate dialogue encoder and summary decoder . |
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Summarization of Dialogues and Conversations At Scale (2023.eacl-tutorials)
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| Challenge: | Conversations are the natural communication format for people. |
| Approach: | This tutorial will survey the cutting-edge methods for summarizing written and spoken conversation. |
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Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization (2023.findings-emnlp)
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| Challenge: | Existing methods for dialogue summarization only apply to specific scenarios and domains. |
| Approach: | They propose a pre-trained model specifically designed for multi-scenario multi-domain dialogue summarization. |
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Few-shot fine-tuning SOTA summarization models for medical dialogues (2022.naacl-srw)
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| Challenge: | Abstractive summarization of medical dialogues is a challenge for standard training approaches due to the paucity of suitable datasets. |
| Approach: | They propose to use medical dialogues to generate abstractive summaries using transformer-based models with zero-shot and few-shot learning strategies. |
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Leveraging Summarization for Unsupervised Dialogue Topic Segmentation (2024.findings-naacl)
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Aleksei Artemiev, Daniil Parinov, Alexey Grishanov, Ivan Borisov, Alexey Vasilev, Daniil Muravetskii, Aleksey Rezvykh, Aleksei Goncharov, Andrey Savchenko
| Challenge: | Existing methods to segment textual data are difficult to handle for noisy spoken dialogues. |
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| Outcome: | The proposed approach outperforms state-of-the-art methods in unsupervised topic segmentation and requires less setup. |