论文标题
生成事实检查Web索赔的摘要
Generating Fact Checking Summaries for Web Claims
论文作者
论文摘要
我们提出了Sumo，这是一种基于神经注意力的方法，该方法学会根据文本文档（例如新闻文章或Web文档）的证据来确定文本主张的正确性。 Sumo通过从文档中介绍一组多元化的句子来进一步生成提取性摘要，这些句子解释了其关于文本索赔正确性的决定。解决事实检查和证据提取问题的先前方法依赖于索赔和文档词嵌入的简单串联，作为要求驱动注意力重量计算的输入。这样做是为了从文档中提取有助于确定索赔正确性的文档中的显着词和句子。但是，这种索赔驱动的注意力的设计无法正确捕获文档中的上下文信息。我们通过使用改进的索赔和标题指导性的分层关注对有效上下文提示进行层次的关注来改善先前的艺术。我们在数据集上显示了有关政治，医疗保健和环境问题的功效。
We present SUMO, a neural attention-based approach that learns to establish the correctness of textual claims based on evidence in the form of text documents (e.g., news articles or Web documents). SUMO further generates an extractive summary by presenting a diversified set of sentences from the documents that explain its decision on the correctness of the textual claim. Prior approaches to address the problem of fact checking and evidence extraction have relied on simple concatenation of claim and document word embeddings as an input to claim driven attention weight computation. This is done so as to extract salient words and sentences from the documents that help establish the correctness of the claim. However, this design of claim-driven attention does not capture the contextual information in documents properly. We improve on the prior art by using improved claim and title guided hierarchical attention to model effective contextual cues. We show the efficacy of our approach on datasets concerning political, healthcare, and environmental issues.
