- Luís Marujo, José Portêlo, Wang Ling, David Martins de Matos, J. Neto, A. Gershman, J. Carbonell, I. Trancoso, B. Raj. 2015. Privacy-Preserving Multi-Document Summarization. Abstract: 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.
