论文标题
Peersum：抽象性多文件摘要的同行评审数据集
PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization
论文作者
论文摘要
我们使用科学出版物的同行评审Peersum，这是一种新的MDS数据集。我们的数据集不同于现有的MDS数据集，因为我们的摘要（即元评论）具有很高的抽象性，并且它们是源文档（即评论）的真实摘要，并且它也具有源文档之间的分歧。我们发现，当前最新的MDS模型难以为同胞生成高质量的摘要，从而提供了新的研究机会。
We present PeerSum, a new MDS dataset using peer reviews of scientific publications. Our dataset differs from the existing MDS datasets in that our summaries (i.e., the meta-reviews) are highly abstractive and they are real summaries of the source documents (i.e., the reviews) and it also features disagreements among source documents. We found that current state-of-the-art MDS models struggle to generate high-quality summaries for PeerSum, offering new research opportunities.
