Multimodal abstractive summarization (MAS) aims to produce a concise summary given the
multimodal data (text and vision). Existing studies mainly focus on how to effectively use the …

Many-to-many multimodal summarization (M $^ 3$ S) task aims to generate summaries in
any language with document inputs in any language and the corresponding image …

Abstractive summarization (AS) aims to generate more flexible and informative descriptions
than extractive summarization. Nevertheless, it often distorts or fabricates facts in the original …

How to quickly obtain information about crisis events on social media such as Twitter and
Weibo is crucial for follow-up rescue work and the promotion of postdisaster reconstruction …

Neural network models enjoy success on language tasks related to Web documents,
including news and Wikipedia articles. However, the characteristics of scientific publications …

Internet users are benefiting from technologies of abstractive summarization enabling them
to view articles on the internet by reading article summaries only instead of an entire article …

Existing multimodal summarization approaches focus on fusing image features in the
encoding process, ignoring the individualized needs for images when generating different …

Multimodal abstractive summarisation (MAS) aims to generate a textual summary from
multimodal data collection, such as video‐text pairs. Despite the success of recent work, the …

Most current multi-modal summarization methods follow a cascaded manner, where an off-
the-shelf object detector is first used to extract visual features. After that, these visual features …

Multimodal summarization which aims to generate summaries with multimodal inputs, eg,
text and visual features, has attracted much attention in the research community. However …