Name
Abstract | ||
|---|---|---|
In this paper, we investigate the problem of extractive single document summarization. We propose an unsupervised summarization method that is based on extracting and scoring keywords in a document and using them to find the sentences that best represent its content. Keywords are extracted and scored using clustering and dependency graphs of sentences. We test our method using different corpora including news, events and email corpora. We evaluate our method in the context of news summarization and email summarization tasks and compare the results with previously published ones. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ISDA.2012.6416598 | ISDA |
Keywords | Field | DocType |
electronic mail,graph theory,information resources,information retrieval,pattern clustering,text analysis,dependency graph node clusters,email corpora,email summarization,events,extractive single document summarization problem,keyword extraction,keyword scoring,news summarization,unsupervised document summarization,unsupervised summarization method,Dependency graph,Email summarization,Extractive summarization,Louvain clustering,ROUGE | Text graph,Graph theory,Multi-document summarization,Automatic summarization,Text mining,Information retrieval,Computer science,Document summarization,Cluster analysis,Dependency graph | Conference |
ISSN | Citations | PageRank |
2164-7143 | 1 | 0.37 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ayman El-Kilany | 1 | 1 | 1.05 |
| Iman Saleh | 2 | 126 | 12.50 |