Align and Attend: Multimodal Summarization with Dual Contrastive Losses

The goal of multimodal summarization is to extract the most important information from different modalities to form output summaries. Unlike the unimodal summarization, the multimodal summarization task explicitly leverages cross-modal information to help generate more reliable and high-quality summaries. However, existing methods fail to leverage the temporal correspondence between different modalities and ignore the intrinsic correlation between different samples. To address this issue, we introduce Align and Attend Multimodal Summarization (A2Summ), a unified multimodal transformer-based model which can effectively align and attend the multimodal input. In addition, we propose two novel contrastive losses to model both inter-sample and intra-sample correlations. Extensive experiments on two standard video summarization datasets (TVSum and SumMe) and two multimodal summarization datasets (Daily Mail and CNN) demonstrate the superiority of A2Summ, achieving state-of-the-art performances on all datasets. Moreover, we collected a large-scale multimodal summarization dataset BLiSS, which contains livestream videos and transcribed texts with annotated summaries. Our code and dataset are publicly available at ~\url{https://boheumd.github.io/A2Summ/}.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Extractive Text Summarization CNN / Daily Mail A2Summ ROUGE-2 20.31 # 4
ROUGE-1 44.11 # 3
ROUGE-L 35.92 # 13
Supervised Video Summarization SumMe A2Summ F1-score (Canonical) 55.0 # 3
Kendall's Tau 0.108 # 7
Spearman's Rho 0.129 # 7
Supervised Video Summarization TvSum A2Summ F1-score (Canonical) 63.4 # 5
Kendall's Tau 0.137 # 8
Spearman's Rho 0.165 # 8

Methods