论文标题
基于实体的抽象摘要，以提高事实一致性
Entity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency
论文作者
论文摘要
尽管最近的抽象性摘要在自动评估指标上取得了成功，但生成的摘要仍然与源文档呈现事实不一致。在本文中，我们关注实体级别的事实不一致，即减少生成的摘要与源文档之间的不匹配实体。因此，我们提出了一种基于实体的新型跨度机制，并通过全球相关性组成部分探索其扩展。四个摘要数据集的实验结果表明，跨度可以有效地改善实体级别的事实一致性，而单词级别和实体级别的显着性基本上没有变化。该代码可在https://github.com/wendy-xiao/entity基于基础上找到
Despite the success of recent abstractive summarizers on automatic evaluation metrics, the generated summaries still present factual inconsistencies with the source document. In this paper, we focus on entity-level factual inconsistency, i.e. reducing the mismatched entities between the generated summaries and the source documents. We therefore propose a novel entity-based SpanCopy mechanism, and explore its extension with a Global Relevance component. Experiment results on four summarization datasets show that SpanCopy can effectively improve the entity-level factual consistency with essentially no change in the word-level and entity-level saliency. The code is available at https://github.com/Wendy-Xiao/Entity-based-SpanCopy
