Name
Abstract | ||
|---|---|---|
With vast amounts of text being available in electronic format, such as news and social media, automatic multi-document summarization can help extract the most important information. We present and evaluate a novel method for automatic extractive multi-document summarization. The method is purely combinatorial, based on bicliques in the bipartite word-sentence occurrence graph. It is particularly suited for collections of very short, independently written texts often single sentences with many repeated phrases, such as customer reviews of products. The method can run in subquadratic time in the number of documents, which is relevant for the application to large collections of documents. |
Year | Venue | Field |
|---|---|---|
2016 | SOFSEM | Automatic summarization,Multi-document summarization,Graph,Social media,Information retrieval,Word lists by frequency,Computer science,Bipartite graph,Customer reviews,Artificial intelligence,Natural language processing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Azam Sheikh Muhammad | 1 | 34 | 4.45 |
| Peter Damaschke | 2 | 471 | 56.99 |
| Olof Mogren | 3 | 60 | 4.41 |