Visualization-enabled multi-document summarization by Iterative Residual Rescaling
| Autor: | Branimir Boguraev, Rie Ando, Roy J. Byrd, Mary S. Neff |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Linguistics and Language
business.industry Computer science Construct (python library) Space (commercial competition) computer.software_genre Residual Automatic summarization Language and Linguistics Visualization Identification (information) Artificial Intelligence Multi-document summarization Artificial intelligence business Theme (computing) computer Software Natural language processing |
| Zdroj: | Natural Language Engineering. 11:67-86 |
| ISSN: | 1469-8110 1351-3249 |
| DOI: | 10.1017/s1351324904003389 |
| Popis: | This paper describes a novel approach to multi-document summarization, which explicitly addresses the problem of detecting, and retaining for the summary, multiple themes in document collections. We place equal emphasis on the processes of theme identification and theme presentation. For the former, we apply Iterative Residual Rescaling (IRR); for the latter, we argue for graphical display elements. IRR is an algorithm designed to account for correlations between words and to construct multi-dimensional topical space indicative of relationships among linguistic objects (documents, phrases, and sentences). Summaries are composed of objects with certain properties, derived by exploiting the many-to-many relationships in such a space. Given their inherent complexity, our multi-faceted summaries benefit from a visualization environment. We discuss some essential features of such an environment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|