Name
Abstract | ||
|---|---|---|
Automatic keyphrase extraction from web news is a fundamental task for news documents retrieval, summarization, topic detection and tracking, etc. Most existing work generally treats each web news as an isolated document. With the rapidly increasing popularity of Web 2.0 technologies, many web news sites provide various social tools for people to post comments. These comments are highly related to the web news and can be considered as valuable background information which can potentially help improve keyphrase extraction. In this paper we propose a novel method to integrate the comment posts into the task of extracting keyphrases from a web news document. Since comments are typically more casual, conversational, and full of jargon, we introduce several strategies to select useful comments for improving this task. The experimental results show that using comments information can significantly improve keyphrase extraction from web news, especially our comments selection method, using machine learning technology, yields the best result. © 2013 Springer-Verlag. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-37401-2_16 | APWeb |
Keywords | Field | DocType |
comments,keyphrase extraction,machine learning,web news | Jargon,Automatic summarization,Data mining,World Wide Web,Web news,Information retrieval,Computer science,Popularity,Casual | Conference |
Volume | Issue | ISSN |
7808 LNCS | null | 16113349 |
Citations | PageRank | References |
1 | 0.36 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhunchen Luo | 1 | 130 | 14.71 |
| Jintao Tang | 2 | 89 | 14.00 |
| Ting Wang | 3 | 36 | 9.43 |