  Multimodal Sarcasm Understanding (MSU) has a wide range of applications in
the news field such as public opinion analysis and forgery detection. However,
existing MSU benchmarks and approaches usually focus on sentence-level MSU. In
document-level news, sarcasm clues are sparse or small and are often concealed
in long text. Moreover, compared to sentence-level comments like tweets, which
mainly focus on only a few trends or hot topics (e.g., sports events), content
in the news is considerably diverse. Models created for sentence-level MSU may
fail to capture sarcasm clues in document-level news. To fill this gap, we
present a comprehensive benchmark for Document-level Multimodal Sarcasm
Understanding (DocMSU). Our dataset contains 102,588 pieces of news with
text-image pairs, covering 9 diverse topics such as health, business, etc. The
proposed large-scale and diverse DocMSU significantly facilitates the research
of document-level MSU in real-world scenarios. To take on the new challenges
posed by DocMSU, we introduce a fine-grained sarcasm comprehension method to
properly align the pixel-level image features with word-level textual features
in documents. Experiments demonstrate the effectiveness of our method, showing
that it can serve as a baseline approach to the challenging DocMSU. Our code
and dataset are available at https://github.com/Dulpy/DocMSU.
