NewsQuote：基于引文提取与归因构建的事实核查专家推荐数据集
NewsQuote: A Dataset Built on Quote Extraction and Attribution for Expert Recommendation in Fact-Checking
AI总结：
提出专家推荐任务，构建含24,031对引文-说话者的NewsQuote数据集，用BERT提取引文，实验表明文档检索优于专家检索。
摘要：
为了增强在新闻文章中寻找可信证据的能力，我们提出了一项新颖的专家推荐任务，旨在识别特定新闻主题上的可信专家。为实现这一目标，我们描述了一个新颖的NewsQuote数据集的构建过程，该数据集包含来自COVID-19新闻语料库的24,031对引文-说话者配对。我们展示了一个基于BERT的问答模型进行说话者和引文提取的自动化流程。然后，我们将专家推荐表述为文档检索任务，即首先检索相关引文作为识别专家的中间步骤，以及专家检索任务，即直接根据查询在候选专家条件下的概率检索来源。在NewsQuote上的实验结果表明，与专家检索相比，文档检索在识别给定新闻主题的相关专家方面更为有效。
英文摘要：
To enhance the ability to find credible evidence in news articles, we propose a novel task of expert recommendation, which aims to identify trustworthy experts on a specific news topic. To achieve the aim, we describe the construction of a novel NewsQuote dataset consisting of 24,031 quote-speaker pairs that appeared on a COVID-19 news corpus. We demonstrate an automatic pipeline for speaker and quote extraction via a BERT-based Question Answering model. Then, we formulate expert recommendations as document retrieval task by retrieving relevant quotes first as an intermediate step for expert identification, and expert retrieval by directly retrieving sources based on the probability of a query conditional on a candidate expert. Experimental results on NewsQuote show that document retrieval is more effective in identifying relevant experts for a given news topic compared to expert retrieval
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
