Name
Abstract | ||
|---|---|---|
In this paper we present a new algorithm for automatic summarization of specialized texts combining terminological and semantic resources: a term extractor and an ontology. The term extractor provides the list of the terms that are present in the text together their corresponding termhood. The ontology is used to calculate the semantic similarity among the terms found in the main body and those present in the document title. The phrases with the highest score are chosen to take part of the final summary. We evaluate the algorithm with ROUGE, comparing the resulting summaries with the summaries of other summarizers. The sentence selection algorithm was also tested as part of a standalone summarizer. It obtains good results, but there is a space for improvement. |
Year | Venue | Keywords |
|---|---|---|
2010 | LREC 2010 - SEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | automatic summarization |
Field | DocType | Citations |
Semantic similarity,Multi-document summarization,Automatic summarization,Ontology,Information retrieval,Computer science,Selection algorithm,Artificial intelligence,Natural language processing,Extractor,Sentence | Conference | 6 |
PageRank | References | Authors |
0.59 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jorge Vivaldi | 1 | 77 | 15.17 |
| Iria da Cunha | 2 | 145 | 16.51 |
| Juan-Manuel Torres-Moreno | 3 | 359 | 51.36 |
| Patricia Velázquez-Morales | 4 | 48 | 4.84 |