Search (3 results, page 1 of 1)

  • × theme_ss:"Automatisches Abstracting"
  • × year_i:[2000 TO 2010}
  1. Yang, C.C.; Wang, F.L.: Hierarchical summarization of large documents (2008) 0.02 
    0.02471459 = product of:
  0.09885836 = sum of:
    0.09885836 = weight(_text_:objects in 1719) [ClassicSimilarity], result of:
      0.09885836 = score(doc=1719,freq=2.0), product of:
        0.33668926 = queryWeight, product of:
          5.315071 = idf(docFreq=590, maxDocs=44218)
          0.06334615 = queryNorm
        0.29361898 = fieldWeight in 1719, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.315071 = idf(docFreq=590, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1719)
  0.25 = coord(1/4)
    Abstract
    Many automatic text summarization models have been developed in the last decades. Related research in information science has shown that human abstractors extract sentences for summaries based on the hierarchical structure of documents; however, the existing automatic summarization models do not take into account the human abstractor's behavior of sentence extraction and only consider the document as a sequence of sentences during the process of extraction of sentences as a summary. In general, a document exhibits a well-defined hierarchical structure that can be described as fractals - mathematical objects with a high degree of redundancy. In this article, we introduce the fractal summarization model based on the fractal theory. The important information is captured from the source document by exploring the hierarchical structure and salient features of the document. A condensed version of the document that is informatively close to the source document is produced iteratively using the contractive transformation in the fractal theory. The fractal summarization model is the first attempt to apply fractal theory to document summarization. It significantly improves the divergence of information coverage of summary and the precision of summary. User evaluations have been conducted. Results have indicated that fractal summarization is promising and outperforms current summarization techniques that do not consider the hierarchical structure of documents.
  2. Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007) 0.01 
    0.012873792 = product of:
  0.05149517 = sum of:
    0.05149517 = weight(_text_:22 in 948) [ClassicSimilarity], result of:
      0.05149517 = score(doc=948,freq=2.0), product of:
        0.22182742 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.06334615 = queryNorm
        0.23214069 = fieldWeight in 948, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.046875 = fieldNorm(doc=948)
  0.25 = coord(1/4)
    Abstract
    In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems.
  3. Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006) 0.01 
    0.010728161 = product of:
  0.042912643 = sum of:
    0.042912643 = weight(_text_:22 in 5290) [ClassicSimilarity], result of:
      0.042912643 = score(doc=5290,freq=2.0), product of:
        0.22182742 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.06334615 = queryNorm
        0.19345059 = fieldWeight in 5290, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5290)
  0.25 = coord(1/4)
    Date
    22. 7.2006 17:25:48

Authors