KEMoS: A knowledge-enhanced multi-modal summarizing framework for Chinese online meetings
The demand for “online meetings” and “collaborative office work” keeps surging recently, producing an abundant amount of relevant data. How to provide participants with accurate and fast summarizing service has attracted extensive attention. Existing meeting summarizing models overlook the utilizati...
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| Vydáno v: | Neural networks Ročník 178; s. 106417 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.10.2024
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| ISSN: | 0893-6080, 1879-2782, 1879-2782 |
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| Abstract | The demand for “online meetings” and “collaborative office work” keeps surging recently, producing an abundant amount of relevant data. How to provide participants with accurate and fast summarizing service has attracted extensive attention. Existing meeting summarizing models overlook the utilization of multi-modal information and the information offsetting during summarizing. In this paper, we develop a knowledge-enhanced multi-modal summarizing framework. Firstly, we construct a three-layer multi-modal meeting knowledge graph, including basic, knowledge, and multi-modal layer, to integrate meeting information thoroughly. Then, we raise a topic-based hierarchical clustering approach, which considers information entropy and difference simultaneously, to capture the semantic evolution of meetings. Next, we devise a multi-modal enhanced encoding strategy, including a sentence-level cross-modal encoder, a joint loss function, and a knowledge graph embedding module, to learn the meeting and topic-level presentations. Finally, when generating summaries, we design a topic-enhanced decoding strategy for the Transformer decoder which mitigates semantic offsetting with the aid of topic information. Extensive experiments show that our proposed work consistently outperforms state-of-the-art solutions on the Chinese meeting dataset, where the ROUGE-1, ROUGE-2, and ROUGE-L are 49.98%, 21.03%, and 32.03% respectively. |
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| AbstractList | The demand for “online meetings” and “collaborative office work” keeps surging recently, producing an abundant amount of relevant data. How to provide participants with accurate and fast summarizing service has attracted extensive attention. Existing meeting summarizing models overlook the utilization of multi-modal information and the information offsetting during summarizing. In this paper, we develop a knowledge-enhanced multi-modal summarizing framework. Firstly, we construct a three-layer multi-modal meeting knowledge graph, including basic, knowledge, and multi-modal layer, to integrate meeting information thoroughly. Then, we raise a topic-based hierarchical clustering approach, which considers information entropy and difference simultaneously, to capture the semantic evolution of meetings. Next, we devise a multi-modal enhanced encoding strategy, including a sentence-level cross-modal encoder, a joint loss function, and a knowledge graph embedding module, to learn the meeting and topic-level presentations. Finally, when generating summaries, we design a topic-enhanced decoding strategy for the Transformer decoder which mitigates semantic offsetting with the aid of topic information. Extensive experiments show that our proposed work consistently outperforms state-of-the-art solutions on the Chinese meeting dataset, where the ROUGE-1, ROUGE-2, and ROUGE-L are 49.98%, 21.03%, and 32.03% respectively. The demand for "online meetings" and "collaborative office work" keeps surging recently, producing an abundant amount of relevant data. How to provide participants with accurate and fast summarizing service has attracted extensive attention. Existing meeting summarizing models overlook the utilization of multi-modal information and the information offsetting during summarizing. In this paper, we develop a knowledge-enhanced multi-modal summarizing framework. Firstly, we construct a three-layer multi-modal meeting knowledge graph, including basic, knowledge, and multi-modal layer, to integrate meeting information thoroughly. Then, we raise a topic-based hierarchical clustering approach, which considers information entropy and difference simultaneously, to capture the semantic evolution of meetings. Next, we devise a multi-modal enhanced encoding strategy, including a sentence-level cross-modal encoder, a joint loss function, and a knowledge graph embedding module, to learn the meeting and topic-level presentations. Finally, when generating summaries, we design a topic-enhanced decoding strategy for the Transformer decoder which mitigates semantic offsetting with the aid of topic information. Extensive experiments show that our proposed work consistently outperforms state-of-the-art solutions on the Chinese meeting dataset, where the ROUGE-1, ROUGE-2, and ROUGE-L are 49.98%, 21.03%, and 32.03% respectively.The demand for "online meetings" and "collaborative office work" keeps surging recently, producing an abundant amount of relevant data. How to provide participants with accurate and fast summarizing service has attracted extensive attention. Existing meeting summarizing models overlook the utilization of multi-modal information and the information offsetting during summarizing. In this paper, we develop a knowledge-enhanced multi-modal summarizing framework. Firstly, we construct a three-layer multi-modal meeting knowledge graph, including basic, knowledge, and multi-modal layer, to integrate meeting information thoroughly. Then, we raise a topic-based hierarchical clustering approach, which considers information entropy and difference simultaneously, to capture the semantic evolution of meetings. Next, we devise a multi-modal enhanced encoding strategy, including a sentence-level cross-modal encoder, a joint loss function, and a knowledge graph embedding module, to learn the meeting and topic-level presentations. Finally, when generating summaries, we design a topic-enhanced decoding strategy for the Transformer decoder which mitigates semantic offsetting with the aid of topic information. Extensive experiments show that our proposed work consistently outperforms state-of-the-art solutions on the Chinese meeting dataset, where the ROUGE-1, ROUGE-2, and ROUGE-L are 49.98%, 21.03%, and 32.03% respectively. |
| ArticleNumber | 106417 |
| Author | Qi, Peng Yao, Muyan Sun, Yan Tao, Dan |
| Author_xml | – sequence: 1 givenname: Peng orcidid: 0000-0003-0390-5449 surname: Qi fullname: Qi, Peng email: pengqi1@bjtu.edu.cn organization: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China – sequence: 2 givenname: Yan surname: Sun fullname: Sun, Yan email: sunyan@bupt.edu.cn organization: Department of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China – sequence: 3 givenname: Muyan orcidid: 0000-0003-3802-9637 surname: Yao fullname: Yao, Muyan email: muyanyao@bjtu.edu.cn organization: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China – sequence: 4 givenname: Dan surname: Tao fullname: Tao, Dan email: dtao@bjtu.edu.cn organization: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38850635$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.neucom.2019.10.019 10.1016/j.neunet.2022.08.021 10.1016/j.ipm.2021.102536 10.1016/j.eswa.2020.113679 10.1145/3512467 10.18653/v1/2021.naacl-main.58 10.1109/TETC.2020.2996710 10.1016/j.knosys.2022.108636 10.26615/issn.2603-2821.2021_019 10.1145/3459637.3482442 10.1109/CVPR.2019.00852 10.18653/v1/2021.acl-short.135 10.3390/info9090217 10.1109/TASLP.2020.3006731 10.1109/CVPR.2018.00784 10.1016/j.ipm.2020.102341 10.1109/TASLP.2021.3124365 10.1016/j.ipm.2019.102187 10.1145/3448015 10.1016/j.eij.2019.11.001 10.1016/j.neunet.2019.12.022 10.1016/j.patcog.2019.01.006 10.1016/j.ipm.2020.102474 10.18653/v1/2023.emnlp-demo.49 10.1177/10464964211015286 10.1145/2740908.2742751 10.18653/v1/2021.naacl-main.109 10.1016/j.ipm.2020.102359 10.1016/j.neunet.2024.106173 10.1162/tacl_a_00373 10.1145/3419106 |
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| Keywords | Topic-enhanced decoding strategy Multi-modal enhanced encoding strategy Multi-modal meeting knowledge graph Topic-based hierarchical clustering approach |
| Language | English |
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| SubjectTerms | Algorithms China Cluster Analysis East Asian People Humans Knowledge Multi-modal enhanced encoding strategy Multi-modal meeting knowledge graph Neural Networks, Computer Semantics Topic-based hierarchical clustering approach Topic-enhanced decoding strategy Videoconferencing |
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