  SemEval task 4 aims to find a proper option from multiple candidates to
resolve the task of machine reading comprehension. Most existing approaches
propose to concat question and option together to form a context-aware model.
However, we argue that straightforward concatenation can only provide a
coarse-grained context for the MRC task, ignoring the specific positions of the
option relative to the question. In this paper, we propose a novel MRC model by
filling options into the question to produce a fine-grained context (defined as
summary) which can better reveal the relationship between option and question.
We conduct a series of experiments on the given dataset, and the results show
that our approach outperforms other counterparts to a large extent.
