Search (11 results, page 1 of 1)

  • × theme_ss:"Automatisches Abstracting"
  1. Díaz, A.; Gervás, P.: User-model based personalized summarization (2007) 0.07 
    0.073287986 = product of:
  0.29315194 = sum of:
    0.29315194 = weight(_text_:personalized in 952) [ClassicSimilarity], result of:
      0.29315194 = score(doc=952,freq=10.0), product of:
        0.27847424 = queryWeight, product of:
          7.1017675 = idf(docFreq=98, maxDocs=44218)
          0.039211962 = queryNorm
        1.0527076 = fieldWeight in 952, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          7.1017675 = idf(docFreq=98, maxDocs=44218)
          0.046875 = fieldNorm(doc=952)
  0.25 = coord(1/4)
    Abstract
    The potential of summary personalization is high, because a summary that would be useless to decide the relevance of a document if summarized in a generic manner, may be useful if the right sentences are selected that match the user interest. In this paper we defend the use of a personalized summarization facility to maximize the density of relevance of selections sent by a personalized information system to a given user. The personalization is applied to the digital newspaper domain and it used a user-model that stores long and short term interests using four reference systems: sections, categories, keywords and feedback terms. On the other side, it is crucial to measure how much information is lost during the summarization process, and how this information loss may affect the ability of the user to judge the relevance of a given document. The results obtained in two personalization systems show that personalized summaries perform better than generic and generic-personalized summaries in terms of identifying documents that satisfy user preferences. We also considered a user-centred direct evaluation that showed a high level of user satisfaction with the summaries.
  2. Sankarasubramaniam, Y.; Ramanathan, K.; Ghosh, S.: Text summarization using Wikipedia (2014) 0.03 
    0.027312819 = product of:
  0.109251276 = sum of:
    0.109251276 = weight(_text_:personalized in 2693) [ClassicSimilarity], result of:
      0.109251276 = score(doc=2693,freq=2.0), product of:
        0.27847424 = queryWeight, product of:
          7.1017675 = idf(docFreq=98, maxDocs=44218)
          0.039211962 = queryNorm
        0.39232093 = fieldWeight in 2693, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          7.1017675 = idf(docFreq=98, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2693)
  0.25 = coord(1/4)
    Abstract
    Automatic text summarization has been an active field of research for many years. Several approaches have been proposed, ranging from simple position and word-frequency methods, to learning and graph based algorithms. The advent of human-generated knowledge bases like Wikipedia offer a further possibility in text summarization - they can be used to understand the input text in terms of salient concepts from the knowledge base. In this paper, we study a novel approach that leverages Wikipedia in conjunction with graph-based ranking. Our approach is to first construct a bipartite sentence-concept graph, and then rank the input sentences using iterative updates on this graph. We consider several models for the bipartite graph, and derive convergence properties under each model. Then, we take up personalized and query-focused summarization, where the sentence ranks additionally depend on user interests and queries, respectively. Finally, we present a Wikipedia-based multi-document summarization algorithm. An important feature of the proposed algorithms is that they enable real-time incremental summarization - users can first view an initial summary, and then request additional content if interested. We evaluate the performance of our proposed summarizer using the ROUGE metric, and the results show that leveraging Wikipedia can significantly improve summary quality. We also present results from a user study, which suggests that using incremental summarization can help in better understanding news articles.
  3. Kannan, R.; Ghinea, G.; Swaminathan, S.: What do you wish to see? : A summarization system for movies based on user preferences (2015) 0.02 
    0.021850254 = product of:
  0.08740102 = sum of:
    0.08740102 = weight(_text_:personalized in 2683) [ClassicSimilarity], result of:
      0.08740102 = score(doc=2683,freq=2.0), product of:
        0.27847424 = queryWeight, product of:
          7.1017675 = idf(docFreq=98, maxDocs=44218)
          0.039211962 = queryNorm
        0.31385675 = fieldWeight in 2683, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          7.1017675 = idf(docFreq=98, maxDocs=44218)
          0.03125 = fieldNorm(doc=2683)
  0.25 = coord(1/4)
    Abstract
    Video summarization aims at producing a compact version of a full-length video while preserving the significant content of the original video. Movie summarization condenses a full-length movie into a summary that still retains the most significant and interesting content of the original movie. In the past, several movie summarization systems have been proposed to generate a movie summary based on low-level video features such as color, motion, texture, etc. However, a generic summary, which is common to everyone and is produced based only on low-level video features will not satisfy every user. As users' preferences for the summary differ vastly for the same movie, there is a need for a personalized movie summarization system nowadays. To address this demand, this paper proposes a novel system to generate semantically meaningful video summaries for the same movie, which are tailored to the preferences and interests of a user. For a given movie, shots and scenes are automatically detected and their high-level features are semi-automatically annotated. Preferences over high-level movie features are explicitly collected from the user using a query interface. The user preferences are generated by means of a stored-query. Movie summaries are generated at shot level and scene level, where shots or scenes are selected for summary skim based on the similarity measured between shots and scenes, and the user's preferences. The proposed movie summarization system is evaluated subjectively using a sample of 20 subjects with eight movies in the English language. The quality of the generated summaries is assessed by informativeness, enjoyability, relevance, and acceptance metrics and Quality of Perception measures. Further, the usability of the proposed summarization system is subjectively evaluated by conducting a questionnaire survey. The experimental results on the performance of the proposed movie summarization approach show the potential of the proposed system.
  4. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.01 
    0.0053126793 = product of:
  0.021250717 = sum of:
    0.021250717 = product of:
      0.042501435 = sum of:
        0.042501435 = weight(_text_:22 in 6599) [ClassicSimilarity], result of:
          0.042501435 = score(doc=6599,freq=2.0), product of:
            0.1373136 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.039211962 = queryNorm
            0.30952093 = fieldWeight in 6599, 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=6599)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    26. 2.1997 10:22:43
  5. Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996) 0.01 
    0.0053126793 = product of:
  0.021250717 = sum of:
    0.021250717 = product of:
      0.042501435 = sum of:
        0.042501435 = weight(_text_:22 in 6751) [ClassicSimilarity], result of:
          0.042501435 = score(doc=6751,freq=2.0), product of:
            0.1373136 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.039211962 = 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)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    6. 3.1997 16:22:15
  6. Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996) 0.01 
    0.0053126793 = product of:
  0.021250717 = sum of:
    0.021250717 = product of:
      0.042501435 = sum of:
        0.042501435 = weight(_text_:22 in 6974) [ClassicSimilarity], result of:
          0.042501435 = score(doc=6974,freq=2.0), product of:
            0.1373136 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.039211962 = queryNorm
            0.30952093 = fieldWeight in 6974, 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=6974)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
  7. Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007) 0.00 
    0.0039845095 = product of:
  0.015938038 = sum of:
    0.015938038 = product of:
      0.031876076 = sum of:
        0.031876076 = weight(_text_:22 in 948) [ClassicSimilarity], result of:
          0.031876076 = score(doc=948,freq=2.0), product of:
            0.1373136 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.039211962 = 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)
      0.5 = coord(1/2)
  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.
  8. Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006) 0.00 
    0.0033204246 = product of:
  0.013281698 = sum of:
    0.013281698 = product of:
      0.026563397 = sum of:
        0.026563397 = weight(_text_:22 in 5290) [ClassicSimilarity], result of:
          0.026563397 = score(doc=5290,freq=2.0), product of:
            0.1373136 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.039211962 = 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)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    22. 7.2006 17:25:48
  9. Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016) 0.00 
    0.0033204246 = product of:
  0.013281698 = sum of:
    0.013281698 = product of:
      0.026563397 = sum of:
        0.026563397 = weight(_text_:22 in 2640) [ClassicSimilarity], result of:
          0.026563397 = score(doc=2640,freq=2.0), product of:
            0.1373136 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.039211962 = queryNorm
            0.19345059 = fieldWeight in 2640, 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=2640)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    22. 1.2016 12:29:41
  10. Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023) 0.00 
    0.0033204246 = product of:
  0.013281698 = sum of:
    0.013281698 = product of:
      0.026563397 = sum of:
        0.026563397 = weight(_text_:22 in 889) [ClassicSimilarity], result of:
          0.026563397 = score(doc=889,freq=2.0), product of:
            0.1373136 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.039211962 = queryNorm
            0.19345059 = fieldWeight in 889, 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=889)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    22. 1.2023 18:57:12
  11. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.00 
    0.0033204246 = product of:
  0.013281698 = sum of:
    0.013281698 = product of:
      0.026563397 = sum of:
        0.026563397 = weight(_text_:22 in 1012) [ClassicSimilarity], result of:
          0.026563397 = score(doc=1012,freq=2.0), product of:
            0.1373136 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.039211962 = queryNorm
            0.19345059 = fieldWeight in 1012, 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=1012)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    22. 6.2023 14:55:20

Authors

Years