  The integration of multi-document pre-training objectives into language
models has resulted in remarkable improvements in multi-document downstream
tasks. In this work, we propose extending this idea by pre-training a generic
multi-document model from a novel cross-document question answering
pre-training objective. To that end, given a set (or cluster) of
topically-related documents, we systematically generate semantically-oriented
questions from a salient sentence in one document and challenge the model,
during pre-training, to answer these questions while "peeking" into other
topically-related documents. In a similar manner, the model is also challenged
to recover the sentence from which the question was generated, again while
leveraging cross-document information. This novel multi-document QA formulation
directs the model to better recover cross-text informational relations, and
introduces a natural augmentation that artificially increases the pre-training
data. Further, unlike prior multi-document models that focus on either
classification or summarization tasks, our pre-training objective formulation
enables the model to perform tasks that involve both short text generation
(e.g., QA) and long text generation (e.g., summarization). Following this
scheme, we pre-train our model -- termed QAmden -- and evaluate its performance
across several multi-document tasks, including multi-document QA,
summarization, and query-focused summarization, yielding improvements of up to
7%, and significantly outperforms zero-shot GPT-3.5 and GPT-4.
