| Título |
Intra-document and Inter-document Redundancy in Multi-document Summarization |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
15th Mexican International Conference on Artificial Intelligence, MICAI 2016 |
| Resumen |
Multi-document summarization differs from single-document summarization in excessive redundancy of mentions of some events or ideas. We show how the amount of redundancy in a document collection can be used for assigning importance to sentences in multi-document extractive summarization: for instance, an idea could be important if it is redundant across documents because of its popularity; on the other hand, an idea could be important if it is not redundant across documents because of its novelty. We propose an unsupervised graph-based technique that, based on proper similarity measures, allows us to experiment with intra-document and inter-document redundancy. Our experiments on DUC corpora show promising results. |
| Observaciones |
https://link.springer.com/content/pdf/10.1007%2F978-3-319-62434-1_9.pdf
DOI: 10.1007/978-3-319-62434-1 9
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),, v. 10061 |
| Lugar |
Cancún |
| País |
Mexico |
| No. de páginas |
105-115 |
| Vol. / Cap. |
10061 LNAI |
| Inicio |
2016-10-23 |
| Fin |
2016-10-28 |
| ISBN/ISSN |
9783319624334 |
