  Document Visual Question Answering (DocVQA) refers to the task of answering
questions from document images. Existing work on DocVQA only considers
single-page documents. However, in real scenarios documents are mostly composed
of multiple pages that should be processed altogether. In this work we extend
DocVQA to the multi-page scenario. For that, we first create a new dataset,
MP-DocVQA, where questions are posed over multi-page documents instead of
single pages. Second, we propose a new hierarchical method, Hi-VT5, based on
the T5 architecture, that overcomes the limitations of current methods to
process long multi-page documents. The proposed method is based on a
hierarchical transformer architecture where the encoder summarizes the most
relevant information of every page and then, the decoder takes this summarized
information to generate the final answer. Through extensive experimentation, we
demonstrate that our method is able, in a single stage, to answer the questions
and provide the page that contains the relevant information to find the answer,
which can be used as a kind of explainability measure.
