MDS: A Fine-Grained Dataset for Multi-Modal Dialogue Summarization (2024.lrec-main)
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| Challenge: | Summarizing the dialogue into a short message has drawn much attention due to the explosion of various dialogue scenes. |
| Approach: | They develop a multi-modal dialogue summarization dataset to enhance the variety of data available for this research area. |
| Outcome: | The proposed dataset provides a demanding testbed for multi-modal dialogue summarization. |
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Yinhong Liu, Jianfeng He, Hang Su, Ruixue Lian, Yi Nian, Jake W. Vincent, Srikanth Vishnubhotla, Robinson Piramuthu, Saab Mansour
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| Challenge: | Multimodal summarization materials lacking a holistic organization by integrating resources from various modalities. |
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| Challenge: | Current models for dialogue summarization have flaws that may not be well exposed by frequently used metrics such as ROUGE. |
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| Challenge: | Multi-document summarization (MDS) assumes a set of topic-related documents is provided as input. |
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| Challenge: | Multidocument summarization (MDS) aims to compress large document collections into short summaries. |
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| Challenge: | Multi-document summarization (MDS) is a task of combining multiple documents into a concise text paragraph. |
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| Challenge: | Multi-document summarization is a process of generating an informative and concise summary from multiple topic-related documents. |
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