  Annotating long-document question answering (long-document QA) pairs is
time-consuming and expensive. To alleviate the problem, it might be possible to
generate long-document QA pairs via unsupervised question answering (UQA)
methods. However, existing UQA tasks are based on short documents, and can
hardly incorporate long-range information. To tackle the problem, we propose a
new task, named unsupervised long-document question answering (ULQA), aiming to
generate high-quality long-document QA instances in an unsupervised manner.
Besides, we propose AttenWalker, a novel unsupervised method to aggregate and
generate answers with long-range dependency so as to construct long-document QA
pairs. Specifically, AttenWalker is composed of three modules, i.e., span
collector, span linker and answer aggregator. Firstly, the span collector takes
advantage of constituent parsing and reconstruction loss to select informative
candidate spans for constructing answers. Secondly, by going through the
attention graph of a pre-trained long-document model, potentially interrelated
text spans (that might be far apart) could be linked together via an
attention-walking algorithm. Thirdly, in the answer aggregator, linked spans
are aggregated into the final answer via the mask-filling ability of a
pre-trained model. Extensive experiments show that AttenWalker outperforms
previous methods on Qasper and NarrativeQA. In addition, AttenWalker also shows
strong performance in the few-shot learning setting.
