Specifically focusing on the landscape of abstractive text summarization, as opposed to
extractive techniques, this survey presents a comprehensive overview, delving into state-of …

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 …

Multimodal affective computing (MAC) has garnered increasing attention due to its broad
applications in analyzing human behaviors and intentions, especially in text-dominated …

Sentiment analysis is a crucial task that aims to understand people's emotional states and
predict emotional categories based on multimodal information. It consists of several …

With the rise of multimedia content on the internet, Multimodal Summarization has become a
challenging task to help individuals grasp vital information fast. However, previous methods …

Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge
of (partial) false negatives. In this paper, we study this problem from the perspective of …

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 …

The workflow of pretraining and fine-tuning has emerged as a popular paradigm for solving
various NLP and V&L (Vision-and-Language) downstream tasks. With the capacity of …

Current metrics for evaluating factuality for abstractive document summarization have
achieved high correlations with human judgment, but they do not account for the vision …

Multimodal emotion recognition in conversation (MERC) and multimodal emotion-cause pair
extraction (MECPE) have recently garnered significant attention. Emotions are the …