Name
Abstract | ||
|---|---|---|
Multidocument summarization has gained popularity in many real world applications because vital information can be extracted within a short time. Extractive summarization aims to generate a summary of a document or a set of documents by ranking sentences and the ranking results rely heavily on the quality of sentence features. However, almost all previous algorithms require hand-crafted features f... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TCYB.2016.2628402 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Feature extraction,Neural networks,Machine learning,Semantics,Data mining,Computer vision,Computational modeling | Automatic summarization,Ranking,Computer science,Convolutional neural network,Feature extraction,Feature engineering,Artificial intelligence,Word embedding,Artificial neural network,Sentence,Machine learning | Journal |
Volume | Issue | ISSN |
47 | 10 | 2168-2267 |
Citations | PageRank | References |
16 | 0.59 | 49 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| yong zhang | 1 | 47 | 2.49 |
| J. Meng | 2 | 2793 | 174.51 |
| Rui Zhao | 3 | 145 | 9.73 |
| Mahardhika Pratama | 4 | 702 | 50.02 |