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Multimodal abstractive summarization for videos (MAS) requires generating aconcise textual summary to describe the highlights of a video according tomultimodal resources, in our case, the video content and its transcript.Inspired by the success of the large-scale generative pre-trained languagemodel (GPLM) in generating high-quality textual content (e.g., summary), recentMAS methods have proposed to adapt the GPLM to this task by equipping it withthe visual information, which is often obtained through a general-purposevisual feature extractor. However, the generally extracted visual features mayoverlook some summary-worthy visual information, which impedes modelperformance. In this work, we propose a novel approach to learning thesummary-worthy visual representation that facilitates abstractivesummarization. Our method exploits the summary-worthy information from both thecross-modal transcript data and the knowledge that distills from the pseudosummary. Extensive experiments on three public multimodal datasets show thatour method outperforms all competing baselines. Furthermore, with theadvantages of summary-worthy visual information, our model can have asignificant improvement on small datasets or even datasets with limitedtraining data.

Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video