  Despite the recent advancements in abstractive summarization systems
leveraged from large-scale datasets and pre-trained language models, the
factual correctness of the summary is still insufficient. One line of trials to
mitigate this problem is to include a post-editing process that can detect and
correct factual errors in the summary. In building such a post-editing system,
it is strongly required that 1) the process has a high success rate and
interpretability and 2) has a fast running time. Previous approaches focus on
regeneration of the summary using the autoregressive models, which lack
interpretability and require high computing resources. In this paper, we
propose an efficient factual error correction system RFEC based on entities
retrieval post-editing process. RFEC first retrieves the evidence sentences
from the original document by comparing the sentences with the target summary.
This approach greatly reduces the length of text for a system to analyze. Next,
RFEC detects the entity-level errors in the summaries by considering the
evidence sentences and substitutes the wrong entities with the accurate
entities from the evidence sentences. Experimental results show that our
proposed error correction system shows more competitive performance than
baseline methods in correcting the factual errors with a much faster speed.
