Zajic, D.; Dorr, B.J.; Lin, J.; Schwartz, R.: Multi-candidate reduction : sentence compression as a tool for document summarization tasks (2007)
0.03
0.03248564 = product of:
0.11369974 = sum of:
0.05205557 = weight(_text_:processing in 944) [ClassicSimilarity], result of:
0.05205557 = score(doc=944,freq=2.0), product of:
0.1662677 = queryWeight, product of:
4.048147 = idf(docFreq=2097, maxDocs=44218)
0.04107254 = queryNorm
0.3130829 = fieldWeight in 944, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.048147 = idf(docFreq=2097, maxDocs=44218)
0.0546875 = fieldNorm(doc=944)
0.06164417 = weight(_text_:techniques in 944) [ClassicSimilarity], result of:
0.06164417 = score(doc=944,freq=2.0), product of:
0.18093403 = queryWeight, product of:
4.405231 = idf(docFreq=1467, maxDocs=44218)
0.04107254 = queryNorm
0.3406997 = fieldWeight in 944, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.405231 = idf(docFreq=1467, maxDocs=44218)
0.0546875 = fieldNorm(doc=944)
0.2857143 = coord(2/7)
- 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.
- Source
- Information processing and management. 43(2007) no.6, S.1549-1570