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  • × author_ss:"McKeown, K."
  • × type_ss:"a"
  1. McKeown, K.; Robin, J.; Kukich, K.: Generating concise natural language summaries (1995) 0.00 
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    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 
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    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 
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    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

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