| 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. |
| Outcome: | The proposed system could be used in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls. |
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| Challenge: | Existing models for extractive summarization of meetings are unfocused and lack content coverage. |
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| Challenge: | Conversations are the natural communication format for people. |
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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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| Challenge: | a novel graph-based framework for abstractive meeting speech summarization is developed . instead of grammatical, well-segmented sentences, the input is made of often ill-formed and ungrammatically ungrammatized text fragments called utterances. |
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A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining (2020.findings-emnlp)
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| Challenge: | Existing methods of summarizing meetings require complex multi-step pipelines that are intractable. |
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Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs (2021.naacl-main)
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| Challenge: | Abstractive conversation summarization has received much attention, but it suffers from insufficient, redundant, or incorrect content due to the unstructured and complex characteristics of human-human interactions. |
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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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A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents (N18-2)
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Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
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On the Abstractiveness of Neural Document Summarization (D18-1)
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| Challenge: | Recent studies show that document summarization systems are abstractive . authors suggest that automated summarizing systems could be improved . |
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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. |
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