论文标题
部分可观测时空混沌系统的无模型预测
KATSum: Knowledge-aware Abstractive Text Summarization
论文作者
论文摘要
文本摘要被认为是下游任务的一项，并且近年来已经对其进行了广泛的研究。它可以帮助人们从互联网上快速感知信息，包括新闻文章，社交帖子，视频等。大多数现有的研究工作都试图开发摘要模型以产生更好的输出。但是，大多数现有模型的出现局限性出现，包括不忠和事实错误。在本文中，我们提出了一个新颖的模型，称为知识吸引的抽象文本摘要，该模型利用知识图提供的优势来增强标准SEQ2SEQ模型。最重要的是，知识图三重态是从源文本中提取的，并用于为关键字提供关系信息，从而产生连贯且事实无误的摘要。我们通过使用现实世界数据集进行广泛的实验。结果表明，所提出的框架可以有效地利用知识图中的信息，并显着减少摘要中的事实错误。
Text Summarization is recognised as one of the NLP downstream tasks and it has been extensively investigated in recent years. It can assist people with perceiving the information rapidly from the Internet, including news articles, social posts, videos, etc. Most existing research works attempt to develop summarization models to produce a better output. However, advent limitations of most existing models emerge, including unfaithfulness and factual errors. In this paper, we propose a novel model, named as Knowledge-aware Abstractive Text Summarization, which leverages the advantages offered by Knowledge Graph to enhance the standard Seq2Seq model. On top of that, the Knowledge Graph triplets are extracted from the source text and utilised to provide keywords with relational information, producing coherent and factually errorless summaries. We conduct extensive experiments by using real-world data sets. The results reveal that the proposed framework can effectively utilise the information from Knowledge Graph and significantly reduce the factual errors in the summary.
