  Attributing answer text to its source document for information-seeking
questions is crucial for building trustworthy, reliable, and accountable
systems. We formulate a new task of post-hoc answer attribution for long
document comprehension (LDC). Owing to the lack of long-form abstractive and
information-seeking LDC datasets, we refactor existing datasets to assess the
strengths and weaknesses of existing retrieval-based and proposed answer
decomposition and textual entailment-based optimal selection attribution
systems for this task. We throw light on the limitations of existing datasets
and the need for datasets to assess the actual performance of systems on this
task.
