Name
Abstract | ||
|---|---|---|
With the abundance of data that can be accessed quickly now, it has become one of the difficulties for people to find specific information on the web. Many documents are available and it is not easy to read each and every document. As a result, the summary of the multiple texts need to be retrieved by taking the main content or just considering parts that interest the readers most. In this paper, we propose a summary of multi document using a Neuro-Fuzzy Inference System (ANFIS). This model can be trained to identify the most salient summary sentences from the document. We evaluate our proposed model with a current methodology that relied on fuzzy logic approach using ROUGE tool. ANFIS shows better results compared to other methods on the Document Understanding Conference (DUC) corpus. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-76351-4_23 | HYBRID INTELLIGENT SYSTEMS, HIS 2017 |
Keywords | Field | DocType |
ANFIS,Summarization,ROUGE,Multi-document | Automatic summarization,Multi-document summarization,Neuro-fuzzy,Computer science,Fuzzy logic,Specific-information,Artificial intelligence,Natural language processing,Adaptive neuro fuzzy inference system,Salient,Inference system | Conference |
Volume | ISSN | Citations |
734 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Azhari | 1 | 0 | 0.68 |
| yogan jaya kumar | 2 | 2 | 2.48 |
| Ong-sing Goh | 3 | 29 | 8.45 |
| Ngo Hea Choon | 4 | 0 | 0.68 |
| Aditya Pradana | 5 | 0 | 0.34 |