Abstract
In the age of big data, automatic methods for creating summaries of documents become increasingly important. In this paper we propose a novel, unsupervised method for (multi-)document summarization. In an unsupervised and language-independent fashion, this approach relies on the strength of word associations in the set of documents to be summarized. The summaries are generated by picking sentences which cover the most specific word associations of the document(s). We measure the performance on the DUC 2007 dataset. Our experiments indicate that the proposed method is the best-performing unsupervised summarization method in the state-of-the-art that makes no use of human-curated knowledge bases.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval |
| Number of pages | 4 |
| Place of Publication | New York |
| Publisher | ACM |
| Publication date | Jul 2014 |
| Pages | 1023-1026 |
| ISBN (Electronic) | 978-1-4503-2257-7 |
| DOIs | |
| Publication status | Published - Jul 2014 |
| MoE publication type | A4 Article in conference proceedings |
| Event | International ACM SIGIR Conference on Research and Development in Information Retrieval - Gold Coast, Australia Duration: 6 Jul 2014 → 11 Jul 2014 Conference number: 37 |
Fields of Science
- 113 Computer and information sciences