Name
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. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Multi-document summarization,Data mining,Automatic summarization,Information retrieval,Computer science,Hash function,Binary number,Computation |
DocType | Volume | Citations |
Journal | abs/1508.01420 | 2 |
PageRank | References | Authors |
0.37 | 8 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luís Marujo | 1 | 224 | 14.86 |
| Jose Portelo | 2 | 23 | 1.71 |
| Ling Wang | 3 | 884 | 52.37 |
| David Martins de Matos | 4 | 152 | 29.19 |
| João Paulo Neto | 5 | 291 | 32.69 |
| A. Gershman | 6 | 316 | 51.85 |
| Jaime G. Carbonell | 7 | 5019 | 724.15 |
| Isabel Trancoso | 8 | 906 | 113.87 |
| Raj, Bhiksha | 9 | 2094 | 204.63 |