Privacy-Preserving Multi-Document Summarization

Luís Marujo; José Portêlo; Wang Ling; David Martins de Matos; João P. Neto; Anatole Gershman; Jaime Carbonell; Isabel Trancoso; Bhiksha Raj

arXiv · 1508.01420

Privacy-Preserving Multi-Document Summarization

Luís Marujo·José Portêlo·Wang Ling·David Martins de Matos·João P. Neto·Anatole Gershman·Jaime Carbonell·Isabel Trancoso·Bhiksha Raj

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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.

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Luís Marujo, José Portêlo, Wang Ling, David Martins de Matos, João P. Neto, Anatole Gershman, Jaime Carbonell, Isabel Trancoso, Bhiksha Raj. 2015-08-06. Privacy-Preserving Multi-Document Summarization. https://arxiv.org/abs/1508.01420

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