Unlike extractive summarization, abstractive summarization has to fuse
different parts of the source text, which inclines to create fake facts. Our
preliminary study reveals nearly 30% of the outputs from a state-of-the-art
neural summarization system suffer from this problem. While previous
abstractive summarization approaches usually focus on the improvement of
informativeness, we argue that faithfulness is also a vital prerequisite for a
practical abstractive summarization system. To avoid generating fake facts in a
summary, we leverage open information extraction and dependency parse
technologies to extract actual fact descriptions from the source text. The
dual-attention sequence-to-sequence framework is then proposed to force the
generation conditioned on both the source text and the extracted fact
descriptions. Experiments on the Gigaword benchmark dataset demonstrate that
our model can greatly reduce fake summaries by 80%. Notably, the fact
descriptions also bring significant improvement on informativeness since they
often condense the meaning of the source text.

提出了一种基于句法和信息提取的双重注意序列到序列框架，该框架旨在解决抽象汇总中存在的虚假事实问题，并使生成的汇总同时具有信息丰富性和语义准确性。