Align and Attend: Multimodal Summarization with Dual Contrastive Losses

by   Bo He, et al.
University of Maryland
Carnegie Mellon University

The goal of multimodal summarization is to extract the most important information from different modalities to form summaries. Unlike 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  <>.


Video Summarization Based on Video-text Modelling

Modern video summarization methods are based on deep neural networks whi...

Sample Efficient Multimodal Semantic Augmentation for Incremental Summarization

In this work, we develop a prompting approach for incremental summarizat...

VideoXum: Cross-modal Visual and Textural Summarization of Videos

Video summarization aims to distill the most important information from ...

MHMS: Multimodal Hierarchical Multimedia Summarization

Multimedia summarization with multimodal output can play an essential ro...

MultiSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos

Multimodal summarization with multimodal output (MSMO) has emerged as a ...

Contrastive Losses Are Natural Criteria for Unsupervised Video Summarization

Video summarization aims to select the most informative subset of frames...

HIINT: Historical, Intra- and Inter- personal Dynamics Modeling with Cross-person Memory Transformer

Accurately modeling affect dynamics, which refers to the changes and flu...

Please sign up or login with your details

Forgot password? Click here to reset