  Multimedia summarization with multimodal output (MSMO) is a recently explored
application in language grounding. It plays an essential role in real-world
applications, i.e., automatically generating cover images and titles for news
articles or providing introductions to online videos. However, existing methods
extract features from the whole video and article and use fusion methods to
select the representative one, thus usually ignoring the critical structure and
varying semantics. In this work, we propose a Semantics-Consistent Cross-domain
Summarization (SCCS) model based on optimal transport alignment with visual and
textual segmentation. In specific, our method first decomposes both video and
article into segments in order to capture the structural semantics,
respectively. Then SCCS follows a cross-domain alignment objective with optimal
transport distance, which leverages multimodal interaction to match and select
the visual and textual summary. We evaluated our method on three recent
multimodal datasets and demonstrated the effectiveness of our method in
producing high-quality multimodal summaries.
