Search (3 results, page 1 of 1)

  • × author_ss:"McKeown, K."
  1. McKeown, K.; Robin, J.; Kukich, K.: Generating concise natural language summaries (1995) 0.00 
    0.0013089187 = product of:
  0.018324861 = sum of:
    0.018324861 = weight(_text_:information in 2932) [ClassicSimilarity], result of:
      0.018324861 = score(doc=2932,freq=6.0), product of:
        0.0545481 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.031073077 = queryNorm
        0.3359395 = fieldWeight in 2932, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.078125 = fieldNorm(doc=2932)
  0.071428575 = coord(1/14)
    Abstract
    Description of the problems for summary generation, the applications developed (for basket ball games - STREAK and for telephone network planning activity - PLANDOC), the linguistic constructions that the systems use to convey information concisely and the textual constraints that determine what information gets included
    Source
    Information processing and management. 31(1995) no.5, S.703-733
  2. Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996) 0.00 
    8.0189965E-4 = product of:
  0.011226594 = sum of:
    0.011226594 = product of:
      0.033679783 = sum of:
        0.033679783 = weight(_text_:22 in 6751) [ClassicSimilarity], result of:
          0.033679783 = score(doc=6751,freq=2.0), product of:
            0.10881262 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.031073077 = queryNorm
            0.30952093 = fieldWeight in 6751, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=6751)
      0.33333334 = coord(1/3)
  0.071428575 = coord(1/14)
    Date
    6. 3.1997 16:22:15
  3. McKeown, K.; Daume III, H.; Chaturvedi, S.; Paparrizos, J.; Thadani, K.; Barrio, P.; Biran, O.; Bothe, S.; Collins, M.; Fleischmann, K.R.; Gravano, L.; Jha, R.; King, B.; McInerney, K.; Moon, T.; Neelakantan, A.; O'Seaghdha, D.; Radev, D.; Templeton, C.; Teufel, S.: Predicting the impact of scientific concepts using full-text features (2016) 0.00 
    6.5445935E-4 = product of:
  0.009162431 = sum of:
    0.009162431 = weight(_text_:information in 3153) [ClassicSimilarity], result of:
      0.009162431 = score(doc=3153,freq=6.0), product of:
        0.0545481 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.031073077 = queryNorm
        0.16796975 = fieldWeight in 3153, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3153)
  0.071428575 = coord(1/14)
    Abstract
    New scientific concepts, interpreted broadly, are continuously introduced in the literature, but relatively few concepts have a long-term impact on society. The identification of such concepts is a challenging prediction task that would help multiple parties-including researchers and the general public-focus their attention within the vast scientific literature. In this paper we present a system that predicts the future impact of a scientific concept, represented as a technical term, based on the information available from recently published research articles. We analyze the usefulness of rich features derived from the full text of the articles through a variety of approaches, including rhetorical sentence analysis, information extraction, and time-series analysis. The results from two large-scale experiments with 3.8 million full-text articles and 48 million metadata records support the conclusion that full-text features are significantly more useful for prediction than metadata-only features and that the most accurate predictions result from combining the metadata and full-text features. Surprisingly, these results hold even when the metadata features are available for a much larger number of documents than are available for the full-text features.
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.11, S.2684-2696

Authors

Years

Themes