  More tasks in Machine Reading Comprehension(MRC) require, in addition to
answer prediction, the extraction of evidence sentences that support the
answer. However, the annotation of supporting evidence sentences is usually
time-consuming and labor-intensive. In this paper, to address this issue and
considering that most of the existing extraction methods are semi-supervised,
we propose an unsupervised evidence extraction method (U3E). U3E takes the
changes after sentence-level feature erasure in the document as input,
simulating the decline in problem-solving ability caused by human memory
decline. In order to make selections on the basis of fully understanding the
semantics of the original text, we also propose metrics to quickly select the
optimal memory model for this input changes. To compare U3E with typical
evidence extraction methods and investigate its effectiveness in evidence
extraction, we conduct experiments on different datasets. Experimental results
show that U3E is simple but effective, not only extracting evidence more
accurately, but also significantly improving model performance.
