Name
Title | ||
|---|---|---|
Can You Summarize This? Identifying Correlates of Input Difficulty for Multi-Document Summarization |
Abstract | ||
|---|---|---|
Different summarization requirements could make the writing of a good summary more dif- ficult, or easier. Summary length and the char- acteristics of the input are such constraints in- fluencing the quality of a potential summary. In this paper we report the results of a quanti- tative analysis on data from large-scale evalu- ations of multi-document summarization, em- pirically confirming this hypothesis. We fur- ther show that features measuring the cohe- siveness of the input are highly correlated with eventual summary quality and that it is possi- ble to use these as features to predict the diffi- culty of new, unseen, summarization inputs. |
Year | Venue | Field |
|---|---|---|
2008 | ACL | Automatic summarization,Multi-document summarization,Information retrieval,Computer science,Artificial intelligence,Natural language processing |
DocType | Volume | Citations |
Conference | P08-1 | 8 |
PageRank | References | Authors |
0.65 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ani Nenkova | 1 | 1831 | 109.14 |
| Annie Louis | 2 | 443 | 24.78 |