Text Summarization via Hidden Markov Models and Pivoted QR Matrix Decomposition
| dc.contributor.author | Conroy, John | en_US |
| dc.contributor.author | O'Leary, Dianne P. | en_US |
| dc.date.accessioned | 2004-05-31T23:09:06Z | |
| dc.date.available | 2004-05-31T23:09:06Z | |
| dc.date.created | 2001-02 | en_US |
| dc.date.issued | 2001-05-10 | en_US |
| dc.description.abstract | A sentence extract summary of a document is a subset of the document's sentences that contains the main ideas in the document. We present two approaches to generating such summaries. The first uses a pivoted QR decomposition of the term-sentence matrix in order to identify sentences that have ideas that are distinct from those in other sentences. The second is based on a hidden Markov model that judges the likelihood that each sentence should be contained in the summary. We compare the results of these methods with summaries generated by humans, showing that we obtain higher agreement than do earlier methods. (Cross-referenced as UMIACS-TR-2001-11) | en_US |
| dc.format.extent | 658811 bytes | |
| dc.format.mimetype | application/postscript | |
| dc.identifier.uri | http://hdl.handle.net/1903/1119 | |
| dc.language.iso | en_US | |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_US |
| dc.relation.isAvailableAt | University of Maryland (College Park, Md.) | en_US |
| dc.relation.isAvailableAt | Tech Reports in Computer Science and Engineering | en_US |
| dc.relation.isAvailableAt | UMIACS Technical Reports | en_US |
| dc.relation.ispartofseries | UM Computer Science Department; CS-TR-4221 | en_US |
| dc.relation.ispartofseries | UMIACS; UMIACS-TR-2001-11 | en_US |
| dc.title | Text Summarization via Hidden Markov Models and Pivoted QR Matrix Decomposition | en_US |
| dc.type | Technical Report | en_US |