In neural abstractive summarization field, conventional sequence-to-sequence based models often suffer from summarizing the wrong aspect of the document with respect to the main aspect. To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect. Unlike traditional abstractive summarization task, reader-aware summarization confronts two main challenges: (1) Comments are informal and noisy; (2) jointly modeling the news document and the reader comments is challenging. To tackle the above challenges, we design an adversarial learning model named reader-aware summary generator (RASG), which consists of four components: (1) a sequence-to-sequence based summary generator; (2) a reader attention module capturing the reader focused aspects; (3) a supervisor modeling the semantic gap between the generated summary and reader focused aspects; (4) a goal tracker producing the goal for each generation step. The supervisor and the goal tacker are used to guide the training of our framework in an adversarial manner. Extensive experiments are conducted on our large-scale real-world text summarization dataset, and the results show that RASG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations. The experimental results also demonstrate the effectiveness of each module in our framework. We release our large-scale dataset for further research.
翻译:在神经抽象总结领域,基于常规序列到序列的模型往往因总结文件与主要方面有关的错误方面而受到影响。为了解决这一问题,我们建议使用阅读器了解抽象摘要生成任务,利用读者评论来帮助模型更好地总结主要方面。与传统的抽象总结任务不同,阅读器了解总结面临两大挑战:(1) 评论是非正式的和吵闹的;(2) 联合模拟新闻文件和读者评论是具有挑战性的。为了应对上述挑战,我们设计了一个称为读者认识摘要生成器的对抗性学习模型(RASG),由四个部分组成:(1) 基于序列到序列的摘要生成器;(2) 读者注意模块,捕捉读者重点方面;(3) 监督员,模拟生成摘要与读者关注的方面之间的语义差距;(4) 目标追踪器,为每一代步骤制定目标; 监督员和目标塔克用一种对抗性的方式指导我们框架的培训。为了应对上述挑战,我们用大规模现实世界文本汇总生成的概要生成器进行了广泛的实验,我们用大规模文本汇总模型进行我们每个实验性数据模型的模型和结果展示。