Name
Abstract | ||
|---|---|---|
In this paper, we investigate the use of temporal information for improving extractive summarization of historical articles. Our method clusters sentences based on their timestamps and temporal similarity. Each resulting cluster is assigned an importance score which can then be used as a weight in traditional sentence ranking techniques. Temporal importance weighting offers consistent improvements over baseline systems. |
Year | Venue | Keywords |
|---|---|---|
2012 | HLT-NAACL | consistent improvement,temporal similarity,temporal information,ranking technique,baseline system,method clusters sentence,historical article,importance score,temporal event clustering,temporal importance weighting,extractive summarization |
Field | DocType | Citations |
Automatic summarization,Weighting,Information retrieval,Ranking,Computer science,Timestamp,Artificial intelligence,Natural language processing,Cluster analysis,Sentence,Temporal similarity | Conference | 4 |
PageRank | References | Authors |
0.44 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Gung | 1 | 8 | 1.17 |
| Jugal Kalita | 2 | 249 | 21.60 |