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.

长文理解中对答案文本进行归因对于建立可信赖、可靠和负责任的系统至关重要，而缺乏长文崭新性和信息查找性的归因数据集使得我们需要重构现有数据集以评估现有基于检索和提出的答案分解和文本蕴含为基础的最佳选择归因系统在这一任务上的优势和不足，同时也突显了现有数据集的限制和对于评估系统在该任务上实际性能的需求。