In this paper, we propose a multi-task learning-based framework that utilizes a combination of self-supervised and supervised pre-training tasks to learn a generic document representation. We design the network architecture and the pretraining tasks to incorporate the multi-modal document information across text, layout, and image dimensions and allow the network to work with multi-page documents. We showcase the applicability of our pre-training framework on a variety of different real-world document tasks such as document classification, document information extraction, and document retrieval. We conduct exhaustive experiments to compare performance against different ablations of our framework and state-of-the-art baselines. We discuss the current limitations and next steps for our work and make the code available to promote future research in this direction.
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