| Challenge: | Conversations are the natural communication format for people. |
| Approach: | This tutorial will survey the cutting-edge methods for summarizing written and spoken conversation. |
| Outcome: | This tutorial will examine the cutting-edge methods for summarizing written and spoken conversations, covering key sub-areas whose combination is needed for a successful solution. |
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| Challenge: | Recent advances in deep learning have improved language generation systems, opening the door to improved forms of abstractive summarization. |
| Approach: | They propose to use neural encoder-decoder architectures to generate abstractive meeting summarizations that are particularly well-suited for multi-party conversation. |
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Summarizing Speech: A Comprehensive Survey (2025.emnlp-main)
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Fabian Retkowski, Maike Züfle, Andreas Sudmann, Dinah Pfau, Shinji Watanabe, Jan Niehues, Alexander Waibel
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NLP for Conversations: Sentiment, Summarization, and Group Dynamics (C18-3)
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| Challenge: | a tutorial focuses on computational models for conversational structure, summarization and sentiment detection, and group dynamics. |
| Approach: | a tutorial will provide examples of specific NLP tasks for conversational structure, summarization and sentiment detection, and group dynamics. |
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Creating a Data Set of Abstractive Summaries of Turn-labeled Spoken Human-Computer Conversations (2022.lrec-1)
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| Challenge: | Digital recorded written and spoken dialogues are becoming more available due to the growing popularity of online messenger services and chatbots. |
| Approach: | They propose to use Dutch spoken human-computer conversations, an annotation layer of turn labels, and conversational abstractive summaries of user answers to build a conversational agent. |
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How did we get here? Summarizing conversation dynamics (2024.naacl-long)
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Yilun Hua, Nicholas Chernogor, Yuzhe Gu, Seoyeon Jeong, Miranda Luo, Cristian Danescu-Niculescu-Mizil
| Challenge: | Throughout a conversation, the way participants interact with each other is in constant flux. |
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Summary Grounded Conversation Generation (2021.findings-acl)
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| Challenge: | Existing datasets for conversation summarization are small due to the lack of large-scale datasets. |
| Approach: | They propose three approaches to generate summary grounded conversations, and evaluate the generated conversations using automatic measures and human judgements. |
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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. |
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Summarizing Medical Conversations via Identifying Important Utterances (2020.coling-main)
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| Challenge: | Applying natural language processing (NLP) techniques to the medical field is a prevailing trend nowadays and has great potential in many applications, such as key information extraction in medical literature. |
| Approach: | They propose to use a hierarchical encoder-tagger model to generate medical conversation summarization by identifying important utterances. |
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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
| Challenge: | Existing models for dialogue summarization focus on extracting the main events of short conversations, but real-world dialogues are difficult to train. |
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A Bag of Tricks for Dialogue Summarization (2021.emnlp-main)
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| 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. |
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