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.
翻译:尽管在以经过培训的语言模型为基础的神经抽象总结中取得了成功,但一个未决问题是,生成的概要并非始终忠实于输入文件。不忠问题可能有两个原因:(1) 汇总模型无法理解或捕捉输入文本的光条,(2) 对语言模型的模型过度翻版以生成流利但不充分的词句。在这项工作中,我们提出了一个信仰增强的总结模型(FES),目的是解决这两个问题,提高抽象总结模型的忠诚性。关于第一个问题,我们提议使用问答(QA)来审查编码器是否完全掌握输入文件,并能够回答输入中关键信息的问题。对于适当输入词的注意也可以用来规定解析器应如何与源打交道。关于第二个问题,我们提出一个最大误差,目的是防止语言模型过于自信。在两个基准总结模型上进行广泛的实验,比我们可靠的数据模型更加精确地展示了我们XMDM/CS的基线。