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.

本文提出了一种基于实体检索的高效事实错误纠正系统 RFEC，通过检索证据句子来缩短系统分析的文本长度，从而检测摘要中的实体级别错误并替换错误的实体，实验证明我们提出的错误纠正系统比基线方法具有更具竞争力的性能和更快的速度。

通过实体检索进行抽象摘要的事实错误矫正

Factual Error Correction for Abstractive Summaries Using Entity Retrieval

Neural Machine Translation (NMT) systems are known to degrade when confronted
with noisy data, especially when the system is trained only on clean data. In
this paper, we show that augmenting training data with sentences containing
artificially-introduced grammatical errors can make the system more robust to
such errors. In combination with an automatic grammar error correction system,
we can recover 1.5 BLEU out of 2.4 BLEU lost due to grammatical errors. We also
present a set of Spanish translations of the JFLEG grammar error correction
corpus, which allows for testing NMT robustness to real grammatical errors.

本研究研究神经机器翻译系统中的数据噪声问题以及如何通过增加包含人工引入的语法错误句子的训练数据来提高其对错误的鲁棒性，同时提出了一套用于测试神经机器翻译在处理语法错误上的 JFLEG 语法纠错语料库的西班牙语翻译。