  There has been a steady need to precisely extract structured knowledge from
the web (i.e. HTML documents). Given a web page, extracting a structured object
along with various attributes of interest (e.g. price, publisher, author, and
genre for a book) can facilitate a variety of downstream applications such as
large-scale knowledge base construction, e-commerce product search, and
personalized recommendation. Considering each web page is rendered from an HTML
DOM tree, existing approaches formulate the problem as a DOM tree node tagging
task. However, they either rely on computationally expensive visual feature
engineering or are incapable of modeling the relationship among the tree nodes.
In this paper, we propose a novel transferable method, Simplified DOM Trees for
Attribute Extraction (SimpDOM), to tackle the problem by efficiently retrieving
useful context for each node by leveraging the tree structure. We study two
challenging experimental settings: (i) intra-vertical few-shot extraction, and
(ii) cross-vertical fewshot extraction with out-of-domain knowledge, to
evaluate our approach. Extensive experiments on the SWDE public dataset show
that SimpDOM outperforms the state-of-the-art (SOTA) method by 1.44% on the F1
score. We also find that utilizing knowledge from a different vertical
(cross-vertical extraction) is surprisingly useful and helps beat the SOTA by a
further 1.37%.
