  Video Question Answering methods focus on commonsense reasoning and visual
cognition of objects or persons and their interactions over time. Current
VideoQA approaches ignore the textual information present in the video.
Instead, we argue that textual information is complementary to the action and
provides essential contextualisation cues to the reasoning process. To this
end, we propose a novel VideoQA task that requires reading and understanding
the text in the video. To explore this direction, we focus on news videos and
require QA systems to comprehend and answer questions about the topics
presented by combining visual and textual cues in the video. We introduce the
``NewsVideoQA'' dataset that comprises more than $8,600$ QA pairs on $3,000+$
news videos obtained from diverse news channels from around the world. We
demonstrate the limitations of current Scene Text VQA and VideoQA methods and
propose ways to incorporate scene text information into VideoQA methods.
