Despite the success achieved in neural abstractive summarization based on
pre-trained language models, one unresolved issue is that the generated
summaries are not always faithful to the input document. There are two possible
causes of the unfaithfulness problem: (1) the summarization model fails to
understand or capture the gist of the input text, and (2) the model over-relies
on the language model to generate fluent but inadequate words. In this work, we
propose a Faithfulness Enhanced Summarization model (FES), which is designed
for addressing these two problems and improving faithfulness in abstractive
summarization. For the first problem, we propose to use question-answering (QA)
to examine whether the encoder fully grasps the input document and can answer
the questions on the key information in the input. The QA attention on the
proper input words can also be used to stipulate how the decoder should attend
to the source. For the second problem, we introduce a max-margin loss defined
on the difference between the language and the summarization model, aiming to
prevent the overconfidence of the language model. Extensive experiments on two
benchmark summarization datasets, CNN/DM and XSum, demonstrate that our model
significantly outperforms strong baselines. The evaluation of factual
consistency also shows that our model generates more faithful summaries than
baselines.

本研究提出了 Faithfulness Enhanced Summarization (FES) 模型，该模型使用 question-answering (QA) 技术检查 encoder 是否完全理解输入文档，并回答关键信息的问题，并引入 max-margin loss 来防止语言模型的过度自信。实验表明，我们的模型在两个基准摘要数据集 CNN/DM 和 XSum 上显著优于强对比模型，并生成比对比模型更符合事实的摘要。

提高抽象化摘要中的忠实性

Towards Improving Faithfulness in Abstractive Summarization

Compressive summarization systems typically rely on a crafted set of
syntactic rules to determine what spans of possible summary sentences can be
deleted, then learn a model of what to actually delete by optimizing for
content selection (ROUGE). In this work, we propose to relax the rigid
syntactic constraints on candidate spans and instead leave compression
decisions to two data-driven criteria: plausibility and salience. Deleting a
span is plausible if removing it maintains the grammaticality and factuality of
a sentence, and spans are salient if they contain important information from
the summary. Each of these is judged by a pre-trained Transformer model, and
only deletions that are both plausible and not salient can be applied. When
integrated into a simple extraction-compression pipeline, our method achieves
strong in-domain results on benchmark summarization datasets, and human
evaluation shows that the plausibility model generally selects for grammatical
and factual deletions. Furthermore, the flexibility of our approach allows it
to generalize cross-domain: our system fine-tuned on only 500 samples from a
new domain can match or exceed an in-domain extractive model trained on much
more data.

本文提出了一种基于可信度和显著性的数据驱动压缩算法，通过使用一个预训练的 Transformer 模型来判断可能删除的语段是否保持语法正确和事实真实，并且仅删除既可信又不显著的语段。实验结果表明，该算法在基准摘要数据集上表现出色，并且具有跨领域泛化的能力。