Name
Abstract | ||
|---|---|---|
This paper deals with our past and recent research in text summarization. We went from single-document summarization through multi- document summarization to update summarization. We describe the development of our summarizer which is based on latent semantic analysis (LSA). The classical LSA-based summarization model was improved by Iterative Residual Rescaling. We propose the update summarization component which determines the redundancy and novelty of each topic discovered by LSA. Moreover, we have modified the sentence selection component in order to prevent inner summary redundancy. The results of our first participation in TAC/DUC evaluation seem to be promising. |
Year | Venue | Field |
|---|---|---|
2008 | TAC | Residual,Automatic summarization,Information retrieval,Computer science,Redundancy (engineering),Novelty,Latent semantic analysis,Sentence |
DocType | Citations | PageRank |
Conference | 2 | 0.41 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| josef steinberger | 1 | 355 | 26.95 |
| Karel Jezek | 2 | 110 | 11.77 |