State-of-the-art extractive multi-document summarization systems are usually
designed without any concern about privacy issues, meaning that all documents
are open to third parties. In this paper we propose a privacy-preserving
approach to multi-document summarization. Our approach enables other parties to
obtain summaries without learning anything else about the original documents'
content. We use a hashing scheme known as Secure Binary Embeddings to convert
documents representation containing key phrases and bag-of-words into bit
strings, allowing the computation of approximate distances, instead of exact
ones. Our experiments indicate that our system yields similar results to its
non-private counterpart on standard multi-document evaluation datasets.

本文提出了一种保护隐私的方法，使用安全二进制嵌入哈希方案将包含关键短语和词袋的文档表示转换为位串，使其他方可以获取摘要而不学习原始文档内容，实验结果表明我们的系统在标准多文档评估数据集上与非隐私匹配的系统产生相似的结果。