Name
Title | ||
|---|---|---|
SwiftRank: An Unsupervised Statistical Approach of Keyword and Salient Sentence Extraction for Individual Documents. |
Abstract | ||
|---|---|---|
In this paper, we introduce an unsupervised stochastic statistical approach for ranking key-phrases, and identifying the salient sentences within a single document for generic extractive summaries. In particular, we propose a method to perceive the salient information of a text unit which is related to the corresponding title and its leverage depending on the sentence position in a text. Furthermore, the proposed method boosts not only the computational time and speed but it still comprehends the substantial information of a document. The experimental results suggest the proposed method well outperforms the baseline approaches significantly in both keyword extraction and summary sentence extraction. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.procs.2017.08.305 | Procedia Computer Science |
Keywords | Field | DocType |
SwiftRank,Keyword extraction,Sentence extraction,Extractive summarization,Single document summarization,Web documents | Information retrieval,Ranking,Computer science,Keyword extraction,Sentence extraction,Natural language processing,Artificial intelligence,Sentence,Salient | Conference |
Volume | ISSN | Citations |
113 | 1877-0509 | 1 |
PageRank | References | Authors |
0.35 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Htet Myet Lynn | 1 | 2 | 1.72 |
| Eunji Lee | 2 | 176 | 22.33 |
| Chang Choi | 3 | 261 | 39.04 |
| Pan-Koo Kim | 4 | 199 | 31.13 |