  Numerous online stock image libraries offer high quality yet copyright free
images for use in marketing campaigns. To assist advertisers in navigating such
third party libraries, we study the problem of automatically fetching relevant
ad images given the ad text (via a short textual query for images). Motivated
by our observations in logged data on ad image search queries (given ad text),
we formulate a keyword extraction problem, where a keyword extracted from the
ad text (or its augmented version) serves as the ad image query. In this
context, we propose VisualTextRank: an unsupervised method to (i) augment input
ad text using semantically similar ads, and (ii) extract the image query from
the augmented ad text. VisualTextRank builds on prior work on graph based
context extraction (biased TextRank in particular) by leveraging both the text
and image of similar ads for better keyword extraction, and using advertiser
category specific biasing with sentence-BERT embeddings. Using data collected
from the Verizon Media Native (Yahoo Gemini) ad platform's stock image search
feature for onboarding advertisers, we demonstrate the superiority of
VisualTextRank compared to competitive keyword extraction baselines (including
an $11\%$ accuracy lift over biased TextRank). For the case when the stock
image library is restricted to English queries, we show the effectiveness of
VisualTextRank on multilingual ads (translated to English) while leveraging
semantically similar English ads. Online tests with a simplified version of
VisualTextRank led to a 28.7% increase in the usage of stock image search, and
a 41.6% increase in the advertiser onboarding rate in the Verizon Media Native
ad platform.
