Name
Abstract | ||
|---|---|---|
In today's digital era, it becomes a challenge for netizens to find specific information on the internet. Many web-based documents are retrieved and it is not easy to digest all the retrieved information. Automatic text summarization is a process that identifies the important points from all the related documents to produce a concise summary. In this paper, we propose a text summarization model based on classification using neuro-fuzzy approach. The model can be trained to filter high-quality summary sentences. We then compare the performance of our proposed model with the existing approaches, which are based on fuzzy logic and neural network techniques. ANFIS showed improved results compared to the previous techniques in terms of average precision, recall and F-measure on the Document Understanding Conference (DUC) data corpus. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1080/24751839.2017.1364040 | JOURNAL OF INFORMATION AND TELECOMMUNICATION |
Keywords | DocType | Volume |
Text summarization, ANFIS, fuzzy logic, neural network, classification | Journal | 1 |
Issue | ISSN | Citations |
4 | 2475-1839 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Azhari | 1 | 0 | 0.68 |
| Yogan Jaya Kumar | 2 | 52 | 6.11 |