  With the widespread popularity of user-generated short videos, it becomes
increasingly challenging for content creators to promote their content to
potential viewers. Automatically generating appealing titles and covers for
short videos can help grab viewers' attention. Existing studies on video
captioning mostly focus on generating factual descriptions of actions, which do
not conform to video titles intended for catching viewer attention.
Furthermore, research for cover selection based on multimodal information is
sparse. These problems motivate the need for tailored methods to specifically
support the joint task of short video title generation and cover selection
(TG-CS) as well as the demand for creating corresponding datasets to support
the studies. In this paper, we first collect and present a real-world dataset
named Short Video Title Generation (SVTG) that contains videos with appealing
titles and covers. We then propose a Title generation and Cover selection with
attention Refinement (TCR) method for TG-CS. The refinement procedure
progressively selects high-quality samples and highly relevant frames and text
tokens within each sample to refine model training. Extensive experiments show
that our TCR method is superior to various existing video captioning methods in
generating titles and is able to select better covers for noisy real-world
short videos.
