Name
Abstract | ||
|---|---|---|
Product and service reviews are abundantly available online, but selecting relevant information from them involves a significant amount of time. The authors address this problem with Starlet, a novel approach for extracting multidocument summarizations that considers aspect rating distributions and language modeling. These features encourage the inclusion of sentences in the summary that preserve the overall opinion distribution and reflect the reviews' original language. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/MIS.2013.36 | IEEE Intelligent Systems |
Keywords | Field | DocType |
Internet,information retrieval,reviews,text analysis,Starlet,aspect rating distributions,information selection,language modeling,multidocument summarization extraction,online reviews summarization,opinion distribution,product review,sentences,service reviews,Computational linguistics,Computational modeling,Data mining,Feature extraction,Natural language processing,Predictive models,Text analysis,reviews summarization,rating prediction models,A* search | Data mining,Information retrieval,Computer science,Computational linguistics,Feature extraction,Artificial intelligence,Natural language processing,Language model,The Internet | Journal |
Volume | Issue | ISSN |
28 | 3 | 1541-1672 |
Citations | PageRank | References |
8 | 0.62 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giuseppe Di Fabbrizio | 1 | 330 | 44.45 |
| Ahmet Aker | 2 | 267 | 30.75 |
| Robert Gaizauskas | 3 | 923 | 121.46 |