  In e-commerce, consumer-generated videos, which in general deliver consumers'
individual preferences for the different aspects of certain products, are
massive in volume. To recommend these videos to potential consumers more
effectively, diverse and catchy video titles are critical. However,
consumer-generated videos seldom accompany appropriate titles. To bridge this
gap, we integrate comprehensive sources of information, including the content
of consumer-generated videos, the narrative comment sentences supplied by
consumers, and the product attributes, in an end-to-end modeling framework.
Although automatic video titling is very useful and demanding, it is much less
addressed than video captioning. The latter focuses on generating sentences
that describe videos as a whole while our task requires the product-aware
multi-grained video analysis. To tackle this issue, the proposed method
consists of two processes, i.e., granular-level interaction modeling and
abstraction-level story-line summarization. Specifically, the granular-level
interaction modeling first utilizes temporal-spatial landmark cues, descriptive
words, and abstractive attributes to builds three individual graphs and
recognizes the intra-actions in each graph through Graph Neural Networks (GNN).
Then the global-local aggregation module is proposed to model inter-actions
across graphs and aggregate heterogeneous graphs into a holistic graph
representation. The abstraction-level story-line summarization further
considers both frame-level video features and the holistic graph to utilize the
interactions between products and backgrounds, and generate the story-line
topic of the video. We collect a large-scale dataset accordingly from
real-world data in Taobao, a world-leading e-commerce platform, and will make
the desensitized version publicly available to nourish further development of
the research community...
