Zajic, D.; Dorr, B.J.; Lin, J.; Schwartz, R.: Multi-candidate reduction : sentence compression as a tool for document summarization tasks (2007)
0.00
0.001789391 = product of:
0.014315128 = sum of:
0.014315128 = product of:
0.042945385 = sum of:
0.042945385 = weight(_text_:problem in 944) [ClassicSimilarity], result of:
0.042945385 = score(doc=944,freq=2.0), product of:
0.13082431 = queryWeight, product of:
4.244485 = idf(docFreq=1723, maxDocs=44218)
0.030822188 = queryNorm
0.3282676 = fieldWeight in 944, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.244485 = idf(docFreq=1723, maxDocs=44218)
0.0546875 = fieldNorm(doc=944)
0.33333334 = coord(1/3)
0.125 = coord(1/8)
- Abstract
- This article examines the application of two single-document sentence compression techniques to the problem of multi-document summarization-a "parse-and-trim" approach and a statistical noisy-channel approach. We introduce the multi-candidate reduction (MCR) framework for multi-document summarization, in which many compressed candidates are generated for each source sentence. These candidates are then selected for inclusion in the final summary based on a combination of static and dynamic features. Evaluations demonstrate that sentence compression is a valuable component of a larger multi-document summarization framework.
