Name
Abstract | ||
|---|---|---|
NESS, RALI's summarization system for the TAC 2008's update task, brings im- provements and continuation to our last year's "all-symbolic" approach. The most distinctive feature of our system is to rely on the syntactical parser FIPS to ex- tract linguistic knowledge from source documents. NESS selects sentences based on linguistic metrics, especially tf · idf scores that measure the relevance of the newswire article sentences to the given topic. It also measures the similarity be- tween candidate sentences and the previ- ous articles already read by the user. NESS ranked well in the competition, obtaining excellent scores in linguistic quality and overall responsiveness. |
Year | Venue | Field |
|---|---|---|
2008 | TAC | Automatic summarization,Data mining,Information retrieval,Ranking,Computer science,Continuation,Artificial intelligence,Natural language processing,Distinctive feature,Source document,Parsing |
DocType | Citations | PageRank |
Conference | 3 | 0.61 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pierre-Etienne Genest | 1 | 66 | 4.34 |
| Guy Lapalme | 2 | 1420 | 105.55 |
| luka nerima | 3 | 82 | 11.43 |
| eric wehrli | 4 | 29 | 6.20 |