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    <abstract>Many-to-many multimodal summarization (M³S) task aims to generate summaries in any language with document inputs in any language and the corresponding image sequence, which essentially comprises of multimodal monolingual summarization (MMS) and multimodal cross-lingual summarization (MXLS) tasks. Although much work has been devoted to either MMS or MXLS, little research pays attention to the M³S task. Besides, existing studies mainly focus on 1) utilizing MMS to enhance MXLS via knowledge distillation without considering the performance of MMS or 2) improving MMS models by filtering summary-unrelated visual features with implicit learning or explicitly complex training objectives. In this paper, we first introduce a general and practical task, i.e., M³S. Further, we propose a dual knowledge distillation and target-oriented vision modeling framework for the M³S task. Specifically, the dual knowledge distillation method guarantees that the knowledge of MMS and MXLS can be transferred to each other and thus mutually prompt both of them. To offer target-oriented visual features, a simple yet effective target-oriented contrastive objective is designed and responsible for discarding needless visual information. Extensive experiments on the many-to-many setting show the effectiveness of the proposed approach. Additionally, we contribute a many-to-many multimodal summarization (lmttM³Sum) dataset with 44 languages to facilitate future research.</abstract>
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%0 Conference Proceedings
%T D²TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization
%A Liang, Yunlong
%A Meng, Fandong
%A Wang, Jiaan
%A Xu, Jinan
%A Chen, Yufeng
%A Zhou, Jie
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F liang-etal-2023-d2tv
%X Many-to-many multimodal summarization (M³S) task aims to generate summaries in any language with document inputs in any language and the corresponding image sequence, which essentially comprises of multimodal monolingual summarization (MMS) and multimodal cross-lingual summarization (MXLS) tasks. Although much work has been devoted to either MMS or MXLS, little research pays attention to the M³S task. Besides, existing studies mainly focus on 1) utilizing MMS to enhance MXLS via knowledge distillation without considering the performance of MMS or 2) improving MMS models by filtering summary-unrelated visual features with implicit learning or explicitly complex training objectives. In this paper, we first introduce a general and practical task, i.e., M³S. Further, we propose a dual knowledge distillation and target-oriented vision modeling framework for the M³S task. Specifically, the dual knowledge distillation method guarantees that the knowledge of MMS and MXLS can be transferred to each other and thus mutually prompt both of them. To offer target-oriented visual features, a simple yet effective target-oriented contrastive objective is designed and responsible for discarding needless visual information. Extensive experiments on the many-to-many setting show the effectiveness of the proposed approach. Additionally, we contribute a many-to-many multimodal summarization (lmttM³Sum) dataset with 44 languages to facilitate future research.
%R 10.18653/v1/2023.findings-emnlp.994
%U https://aclanthology.org/2023.findings-emnlp.994
%U https://doi.org/10.18653/v1/2023.findings-emnlp.994
%P 14910-14922

Markdown (Informal)

[D2TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization](https://aclanthology.org/2023.findings-emnlp.994) (Liang et al., Findings 2023)

• D2TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization (Liang et al., Findings 2023)

ACL

• Yunlong Liang, Fandong Meng, Jiaan Wang, Jinan Xu, Yufeng Chen, and Jie Zhou. 2023. D2TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14910–14922, Singapore. Association for Computational Linguistics.