Name
Abstract | ||
|---|---|---|
Automated text summarization is important to for humans to better manage the massive information explosion. Several machine learning approaches could be successfully used to handle the problem. This paper reports the results of our study to compare the performance between neural networks and support vector machines for text summarization. Both models have the ability to discover non-linear data and are effective model when dealing with large datasets. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1806338.1806429 | iiWAS |
Keywords | Field | DocType |
support vector machine,neural network,effective model,automated text summarization,text summarization technique,large datasets,massive information explosion,text summarization,non-linear data,feature selection,machine learning,svm | Automatic summarization,Data mining,Feature selection,Computer science,Support vector machine,Artificial intelligence,Artificial neural network,Information explosion,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.35 | 6 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keivan Kianmehr | 1 | 187 | 20.39 |
| Shang Gao | 2 | 291 | 59.33 |
| Jawad Attari | 3 | 1 | 0.35 |
| M. Mushfiqur Rahman | 4 | 1 | 0.35 |
| Kofi Akomeah | 5 | 1 | 0.35 |
| Reda Alhajj | 6 | 1919 | 205.67 |
| Jon Rokne | 7 | 104 | 15.89 |
| Ken Barker | 8 | 834 | 83.23 |