Search (1 results, page 1 of 1)

  • × author_ss:"Schwartz, R."
  • × author_ss:"Zajic, D."
  • × theme_ss:"Automatisches Abstracting"
  1. Zajic, D.; Dorr, B.J.; Lin, J.; Schwartz, R.: Multi-candidate reduction : sentence compression as a tool for document summarization tasks (2007) 0.00
    0.0016464829 = product of:
      0.014818345 = sum of:
        0.014818345 = weight(_text_:of in 944) [ClassicSimilarity], result of:
          0.014818345 = score(doc=944,freq=8.0), product of:
            0.061262865 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03917671 = queryNorm
            0.24188137 = fieldWeight in 944, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0546875 = fieldNorm(doc=944)
      0.11111111 = coord(1/9)
    
    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.