Name
Abstract | ||
|---|---|---|
We describe a task-based evaluation to determine whether multi-document summaries measurably improve user performance whe using online news browsing systems for directed research. We evaluated the multi-document summaries generated by Newsblaster, a robust news browsing system that clusters online news articles and summarizes multiple articles on each event. Four groups of subjects were asked to perform the same time-restricted fact-gathering tasks, reading news under different conditions: no summaries at all, single sentence summaries drawn from one of the articles, Newsblaster multi-document summaries, and human summaries. Our results show that, in comparison to source documents only, the quality of reports assembled using Newsblaster summaries was significantly better and user satisfaction was higher with both Newsblaster and human summaries. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1076034.1076072 | SIGIR |
Keywords | Field | DocType |
different condition,newsblaster summary,online news,multi-document summary,user satisfaction,user performance whe,robust news,human summary,clusters online news article,newsblaster multi-document summary,text summarization,evaluation | Automatic summarization,Information retrieval,Computer science,Source document,Sentence | Conference |
ISBN | Citations | PageRank |
1-59593-034-5 | 25 | 1.33 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kathleen R. McKeown | 1 | 4990 | 741.29 |
| Rebecca J. Passonneau | 2 | 978 | 160.46 |
| David K. Elson | 3 | 202 | 14.25 |
| Ani Nenkova | 4 | 1831 | 109.14 |
| Julia Hirschberg | 5 | 2982 | 448.62 |