  Current question answering (QA) systems primarily consider the single-answer
scenario, where each question is assumed to be paired with one correct answer.
However, in many real-world QA applications, multiple answer scenarios arise
where consolidating answers into a comprehensive and non-redundant set of
answers is a more efficient user interface. In this paper, we formulate the
problem of answer consolidation, where answers are partitioned into multiple
groups, each representing different aspects of the answer set. Then, given this
partitioning, a comprehensive and non-redundant set of answers can be
constructed by picking one answer from each group. To initiate research on
answer consolidation, we construct a dataset consisting of 4,699 questions and
24,006 sentences and evaluate multiple models. Despite a promising performance
achieved by the best-performing supervised models, we still believe this task
has room for further improvements.
