Search (31 results, page 1 of 2)

  • × theme_ss:"Automatisches Abstracting"
  1. Johnson, F.: Automatic abstracting research (1995) 0.02 
    0.021078197 = product of:
  0.06323459 = sum of:
    0.06323459 = weight(_text_:f in 3847) [ClassicSimilarity], result of:
      0.06323459 = score(doc=3847,freq=2.0), product of:
        0.17949225 = queryWeight, product of:
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.04503309 = queryNorm
        0.35229704 = fieldWeight in 3847, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.0625 = fieldNorm(doc=3847)
  0.33333334 = coord(1/3)
  2. Harabagiu, S.; Hickl, A.; Lacatusu, F.: Satisfying information needs with multi-document summaries (2007) 0.02 
    0.021078197 = product of:
  0.06323459 = sum of:
    0.06323459 = weight(_text_:f in 939) [ClassicSimilarity], result of:
      0.06323459 = score(doc=939,freq=2.0), product of:
        0.17949225 = queryWeight, product of:
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.04503309 = queryNorm
        0.35229704 = fieldWeight in 939, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.0625 = fieldNorm(doc=939)
  0.33333334 = coord(1/3)
  3. Uyttendaele, C.; Moens, M.-F.; Dumortier, J.: SALOMON: automatic abstracting of legal cases for effective access to court decisions (1998) 0.02 
    0.018443422 = product of:
  0.055330265 = sum of:
    0.055330265 = weight(_text_:f in 495) [ClassicSimilarity], result of:
      0.055330265 = score(doc=495,freq=2.0), product of:
        0.17949225 = queryWeight, product of:
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.04503309 = queryNorm
        0.3082599 = fieldWeight in 495, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.0546875 = fieldNorm(doc=495)
  0.33333334 = coord(1/3)
  4. Moens, M.-F.: Summarizing court decisions (2007) 0.02 
    0.018443422 = product of:
  0.055330265 = sum of:
    0.055330265 = weight(_text_:f in 954) [ClassicSimilarity], result of:
      0.055330265 = score(doc=954,freq=2.0), product of:
        0.17949225 = queryWeight, product of:
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.04503309 = queryNorm
        0.3082599 = fieldWeight in 954, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.0546875 = fieldNorm(doc=954)
  0.33333334 = coord(1/3)
  5. Moens, M.-F.; Uyttendaele, C.: Automatic text structuring and categorization as a first step in summarizing legal cases (1997) 0.02 
    0.015808647 = product of:
  0.04742594 = sum of:
    0.04742594 = weight(_text_:f in 2256) [ClassicSimilarity], result of:
      0.04742594 = score(doc=2256,freq=2.0), product of:
        0.17949225 = queryWeight, product of:
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.04503309 = queryNorm
        0.26422277 = fieldWeight in 2256, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.046875 = fieldNorm(doc=2256)
  0.33333334 = coord(1/3)
  6. Moens, M.-F.; Uyttendaele, C.; Dumotier, J.: Abstracting of legal cases : the potential of clustering based on the selection of representative objects (1999) 0.02 
    0.015808647 = product of:
  0.04742594 = sum of:
    0.04742594 = weight(_text_:f in 2944) [ClassicSimilarity], result of:
      0.04742594 = score(doc=2944,freq=2.0), product of:
        0.17949225 = queryWeight, product of:
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.04503309 = queryNorm
        0.26422277 = fieldWeight in 2944, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.046875 = fieldNorm(doc=2944)
  0.33333334 = coord(1/3)
  7. Liang, S.-F.; Devlin, S.; Tait, J.: Investigating sentence weighting components for automatic summarisation (2007) 0.02 
    0.015808647 = product of:
  0.04742594 = sum of:
    0.04742594 = weight(_text_:f in 899) [ClassicSimilarity], result of:
      0.04742594 = score(doc=899,freq=2.0), product of:
        0.17949225 = queryWeight, product of:
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.04503309 = queryNorm
        0.26422277 = fieldWeight in 899, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.046875 = fieldNorm(doc=899)
  0.33333334 = coord(1/3)
  8. Martinez-Romo, J.; Araujo, L.; Fernandez, A.D.: SemGraph : extracting keyphrases following a novel semantic graph-based approach (2016) 0.02 
    0.015808647 = product of:
  0.04742594 = sum of:
    0.04742594 = weight(_text_:f in 2832) [ClassicSimilarity], result of:
      0.04742594 = score(doc=2832,freq=2.0), product of:
        0.17949225 = queryWeight, product of:
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.04503309 = queryNorm
        0.26422277 = fieldWeight in 2832, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.046875 = fieldNorm(doc=2832)
  0.33333334 = coord(1/3)
    Abstract
    Keyphrases represent the main topics a text is about. In this article, we introduce SemGraph, an unsupervised algorithm for extracting keyphrases from a collection of texts based on a semantic relationship graph. The main novelty of this algorithm is its ability to identify semantic relationships between words whose presence is statistically significant. Our method constructs a co-occurrence graph in which words appearing in the same document are linked, provided their presence in the collection is statistically significant with respect to a null model. Furthermore, the graph obtained is enriched with information from WordNet. We have used the most recent and standardized benchmark to evaluate the system ability to detect the keyphrases that are part of the text. The result is a method that achieves an improvement of 5.3% and 7.28% in F measure over the two labeled sets of keyphrases used in the evaluation of SemEval-2010.
  9. Yeh, J.-Y.; Ke, H.-R.; Yang, W.-P.; Meng, I.-H.: Text summarization using a trainable summarizer and latent semantic analysis (2005) 0.01 
    0.013173874 = product of:
  0.03952162 = sum of:
    0.03952162 = weight(_text_:f in 1003) [ClassicSimilarity], result of:
      0.03952162 = score(doc=1003,freq=2.0), product of:
        0.17949225 = queryWeight, product of:
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.04503309 = queryNorm
        0.22018565 = fieldWeight in 1003, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1003)
  0.33333334 = coord(1/3)
    Abstract
    This paper proposes two approaches to address text summarization: modified corpus-based approach (MCBA) and LSA-based T.R.M. approach (LSA + T.R.M.). The first is a trainable summarizer, which takes into account several features, including position, positive keyword, negative keyword, centrality, and the resemblance to the title, to generate summaries. Two new ideas are exploited: (1) sentence positions are ranked to emphasize the significances of different sentence positions, and (2) the score function is trained by the genetic algorithm (GA) to obtain a suitable combination of feature weights. The second uses latent semantic analysis (LSA) to derive the semantic matrix of a document or a corpus and uses semantic sentence representation to construct a semantic text relationship map. We evaluate LSA + T.R.M. both with single documents and at the corpus level to investigate the competence of LSA in text summarization. The two novel approaches were measured at several compression rates on a data corpus composed of 100 political articles. When the compression rate was 30%, an average f-measure of 49% for MCBA, 52% for MCBA + GA, 44% and 40% for LSA + T.R.M. in single-document and corpus level were achieved respectively.
  10. Sweeney, S.; Crestani, F.; Losada, D.E.: 'Show me more' : incremental length summarisation using novelty detection (2008) 0.01 
    0.013173874 = product of:
  0.03952162 = sum of:
    0.03952162 = weight(_text_:f in 2054) [ClassicSimilarity], result of:
      0.03952162 = score(doc=2054,freq=2.0), product of:
        0.17949225 = queryWeight, product of:
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.04503309 = queryNorm
        0.22018565 = fieldWeight in 2054, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2054)
  0.33333334 = coord(1/3)
  11. Wei, F.; Li, W.; Lu, Q.; He, Y.: Applying two-level reinforcement ranking in query-oriented multidocument summarization (2009) 0.01 
    0.013173874 = product of:
  0.03952162 = sum of:
    0.03952162 = weight(_text_:f in 3120) [ClassicSimilarity], result of:
      0.03952162 = score(doc=3120,freq=2.0), product of:
        0.17949225 = queryWeight, product of:
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.04503309 = queryNorm
        0.22018565 = fieldWeight in 3120, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3120)
  0.33333334 = coord(1/3)
  12. Galgani, F.; Compton, P.; Hoffmann, A.: Summarization based on bi-directional citation analysis (2015) 0.01 
    0.013173874 = product of:
  0.03952162 = sum of:
    0.03952162 = weight(_text_:f in 2685) [ClassicSimilarity], result of:
      0.03952162 = score(doc=2685,freq=2.0), product of:
        0.17949225 = queryWeight, product of:
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.04503309 = queryNorm
        0.22018565 = fieldWeight in 2685, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.985786 = idf(docFreq=2232, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2685)
  0.33333334 = coord(1/3)
  13. Dammeyer, A.; Jürgensen, W.; Krüwel, C.; Poliak, E.; Ruttkowski, S.; Schäfer, Th.; Sirava, M.; Hermes, T.: Videoanalyse mit DiVA (1998) 0.01 
    0.009370025 = product of:
  0.028110076 = sum of:
    0.028110076 = product of:
      0.05622015 = sum of:
        0.05622015 = weight(_text_:von in 23) [ClassicSimilarity], result of:
          0.05622015 = score(doc=23,freq=14.0), product of:
            0.12014598 = queryWeight, product of:
              2.6679487 = idf(docFreq=8340, maxDocs=44218)
              0.04503309 = queryNorm
            0.46793205 = fieldWeight in 23, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              2.6679487 = idf(docFreq=8340, maxDocs=44218)
              0.046875 = fieldNorm(doc=23)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
    Abstract
    Die Bedeutung von Videos nimmt für multimediale Systeme stetig zu. Dabei existiert eine Vielzahl von Produkten zur Betrachtung von Videos, allerdings nur wenige Ansätze, den Inhalt eines Videos zu erschließen. Das DiVA-System, welches an der Universität Bremen im Rahmen eines studentischen Projektes entwickelt wird, dient der automatischen Analyse von MPEG-I Videofilmen. Der dabei verfolgte Ansatz läßt sich in vier Phasen gliedern. Zunächst wird der Videofilm durch eine Shotanalyse in seine einzelnen Kameraeinstellungen (Shots) unterteilt. Darauf aufbauend findet eine Kamerabewegungsanalyse sowie die Erstellung von Mosaicbildern statt. Mit Methoden der künstlichen Intelligenz und der digitalen Bildverarbeitung wird das analysierte Material nach Bild- und Toninformationen ausgewertet. Das Resultat ist eine textuelle Beschreibung eines Videofilms, auf der mit Hilfe von Text-Retrieval-Systemen recherchiert werden kann
    Source
    Inhaltsbezogene Suche von Bildern und Videosequenzen in digitalen multimedialen Archiven: Beiträge eines Workshops der KI'98 am 16./17.9.1998 in Bremen. Hrsg.: N. Luth
  14. Hahn, U.: Automatisches Abstracting (2013) 0.01 
    0.008347482 = product of:
  0.025042446 = sum of:
    0.025042446 = product of:
      0.050084893 = sum of:
        0.050084893 = weight(_text_:von in 721) [ClassicSimilarity], result of:
          0.050084893 = score(doc=721,freq=4.0), product of:
            0.12014598 = queryWeight, product of:
              2.6679487 = idf(docFreq=8340, maxDocs=44218)
              0.04503309 = queryNorm
            0.416867 = fieldWeight in 721, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.6679487 = idf(docFreq=8340, maxDocs=44218)
              0.078125 = fieldNorm(doc=721)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
    Source
    Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried
  15. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.01 
    0.00813515 = product of:
  0.02440545 = sum of:
    0.02440545 = product of:
      0.0488109 = sum of:
        0.0488109 = weight(_text_:22 in 6599) [ClassicSimilarity], result of:
          0.0488109 = score(doc=6599,freq=2.0), product of:
            0.15769821 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04503309 = 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)
  0.33333334 = coord(1/3)
    Date
    26. 2.1997 10:22:43
  16. Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996) 0.01 
    0.00813515 = product of:
  0.02440545 = sum of:
    0.02440545 = product of:
      0.0488109 = sum of:
        0.0488109 = weight(_text_:22 in 6751) [ClassicSimilarity], result of:
          0.0488109 = score(doc=6751,freq=2.0), product of:
            0.15769821 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04503309 = 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)
  0.33333334 = coord(1/3)
    Date
    6. 3.1997 16:22:15
  17. Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996) 0.01 
    0.00813515 = product of:
  0.02440545 = sum of:
    0.02440545 = product of:
      0.0488109 = sum of:
        0.0488109 = weight(_text_:22 in 6974) [ClassicSimilarity], result of:
          0.0488109 = score(doc=6974,freq=2.0), product of:
            0.15769821 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04503309 = 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)
  0.33333334 = coord(1/3)
    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
  18. Kuhlen, R.: Informationsaufbereitung III : Referieren (Abstracts - Abstracting - Grundlagen) (2004) 0.01 
    0.007466215 = product of:
  0.022398645 = sum of:
    0.022398645 = product of:
      0.04479729 = sum of:
        0.04479729 = weight(_text_:von in 2917) [ClassicSimilarity], result of:
          0.04479729 = score(doc=2917,freq=20.0), product of:
            0.12014598 = queryWeight, product of:
              2.6679487 = idf(docFreq=8340, maxDocs=44218)
              0.04503309 = queryNorm
            0.37285718 = fieldWeight in 2917, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              2.6679487 = idf(docFreq=8340, maxDocs=44218)
              0.03125 = fieldNorm(doc=2917)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
    Abstract
    Was ein Abstract (im Folgenden synonym mit Referat oder Kurzreferat gebraucht) ist, legt das American National Standards Institute in einer Weise fest, die sicherlich von den meisten Fachleuten akzeptiert werden kann: "An abstract is defined as an abbreviated, accurate representation of the contents of a document"; fast genauso die deutsche Norm DIN 1426: "Das Kurzreferat gibt kurz und klar den Inhalt des Dokuments wieder." Abstracts gehören zum wissenschaftlichen Alltag. Weitgehend allen Publikationen, zumindest in den naturwissenschaftlichen, technischen, informationsbezogenen oder medizinischen Bereichen, gehen Abstracts voran, "prefe-rably prepared by its author(s) for publication with it". Es gibt wohl keinen Wissenschaftler, der nicht irgendwann einmal ein Abstract geschrieben hätte. Gehört das Erstellen von Abstracts dann überhaupt zur dokumentarischen bzw informationswissenschaftlichen Methodenlehre, wenn es jeder kann? Was macht den informationellen Mehrwert aus, der durch Expertenreferate gegenüber Laienreferaten erzeugt wird? Dies ist nicht so leicht zu beantworten, zumal geeignete Bewertungsverfahren fehlen, die Qualität von Abstracts vergleichend "objektiv" zu messen. Abstracts werden in erheblichem Umfang von Informationsspezialisten erstellt, oft unter der Annahme, dass Autoren selber dafür weniger geeignet sind. Vergegenwärtigen wir uns, was wir über Abstracts und Abstracting wissen. Ein besonders gelungenes Abstract ist zuweilen klarer als der Ursprungstext selber, darf aber nicht mehr Information als dieser enthalten: "Good abstracts are highly structured, concise, and coherent, and are the result of a thorough analysis of the content of the abstracted materials. Abstracts may be more readable than the basis documents, but because of size constraints they rarely equal and never surpass the information content of the basic document". Dies ist verständlich, denn ein "Abstract" ist zunächst nichts anderes als ein Ergebnis des Vorgangs einer Abstraktion. Ohne uns zu sehr in die philosophischen Hintergründe der Abstraktion zu verlieren, besteht diese doch "in der Vernachlässigung von bestimmten Vorstellungsbzw. Begriffsinhalten, von welchen zugunsten anderer Teilinhalte abgesehen, abstrahiert' wird. Sie ist stets verbunden mit einer Fixierung von (interessierenden) Merkmalen durch die aktive Aufmerksamkeit, die unter einem bestimmten pragmatischen Gesichtspunkt als wesentlich' für einen vorgestellten bzw für einen unter einen Begriff fallenden Gegenstand (oder eine Mehrheit von Gegenständen) betrachtet werden". Abstracts reduzieren weniger Begriffsinhalte, sondern Texte bezüglich ihres proportionalen Gehaltes. Borko/ Bernier haben dies sogar quantifiziert; sie schätzen den Reduktionsfaktor auf 1:10 bis 1:12
    Source
    Grundlagen der praktischen Information und Dokumentation. 5., völlig neu gefaßte Ausgabe. 2 Bde. Hrsg. von R. Kuhlen, Th. Seeger u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried. Bd.1: Handbuch zur Einführung in die Informationswissenschaft und -praxis
  19. Ruda, S.: Maschinenunterstützte Kondensierung von Fachtexten mit CONNY : Abstracting am Beispiel eines 'Nachrichten für Dokumentation'-Textkorpus (1994) 0.01 
    0.0071564754 = product of:
  0.021469425 = sum of:
    0.021469425 = product of:
      0.04293885 = sum of:
        0.04293885 = weight(_text_:von in 2376) [ClassicSimilarity], result of:
          0.04293885 = score(doc=2376,freq=6.0), product of:
            0.12014598 = queryWeight, product of:
              2.6679487 = idf(docFreq=8340, maxDocs=44218)
              0.04503309 = queryNorm
            0.357389 = fieldWeight in 2376, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.6679487 = idf(docFreq=8340, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2376)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
    Abstract
    Als Textkorpus sind von 50 verschiedenen Autoren verfaßte Dokumente der Zeitschrift 'Nachrichten für Dokumentation' aus einem Zwanzigjahreszeitraum (1969-1989) herangezogen worden. Die Untersuchung der Abstracts hat ergeben, daß lediglich 15 von 50 Abstracts aus ausschließlich 'normgerechten' Abstractsätzen bestehen und kein Abstract allen Anforderungen der Richtlinien genügt. Insofern signalisieren sie die Abstracting-Richtlinien als 'Wunschdenken', was die Idee des maschinenunterstützten Abstracting nach linguistischen Merkmalen bekräftigt. CONNY ist ein interaktives linhuistisches Abstracting-Modell für Fachtexte, das dem Abstractor auf der Oberflächenstruktur operierende allgemeine Abstracting-Richtlinien anbietet. Es kondendiert die als abstractrelevant bewertenden Primärtextteile auf Primärtext-, Satz- und Abstractebene hinsichtlich Lexik, Syntax und Semantik
  20. Kuhlen, R.: In Richtung Summarizing für Diskurse in K3 (2006) 0.01 
    0.0071564754 = product of:
  0.021469425 = sum of:
    0.021469425 = product of:
      0.04293885 = sum of:
        0.04293885 = weight(_text_:von in 6067) [ClassicSimilarity], result of:
          0.04293885 = score(doc=6067,freq=6.0), product of:
            0.12014598 = queryWeight, product of:
              2.6679487 = idf(docFreq=8340, maxDocs=44218)
              0.04503309 = queryNorm
            0.357389 = fieldWeight in 6067, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.6679487 = idf(docFreq=8340, maxDocs=44218)
              0.0546875 = fieldNorm(doc=6067)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
    Abstract
    Der Bedarf nach Summarizing-Leistungen, in Situationen der Fachinformation, aber auch in kommunikativen Umgebungen (Diskursen) wird aufgezeigt. Summarizing wird dazu in den Kontext des bisherigen (auch automatischen) Abstracting/Extracting gestellt. Der aktuelle Forschungsstand, vor allem mit Blick auf Multi-Document-Summarizing, wird dargestellt. Summarizing ist eine wichtige Funktion in komplex und umfänglich werdenden Diskussionen in elektronischen Foren. Dies wird am Beispiel des e-Learning-Systems K3 aufgezeigt. Rudimentäre Summarizing-Funktionen von K3 und des zugeordneten K3VIS-Systems werden dargestellt. Der Rahmen für ein elaborierteres, Template-orientiertes Summarizing unter Verwendung der vielfältigen Auszeichnungsfunktionen von K3 (Rollen, Diskurstypen, Inhaltstypen etc.) wird aufgespannt.
    Source
    Information und Sprache: Beiträge zu Informationswissenschaft, Computerlinguistik, Bibliothekswesen und verwandten Fächern. Festschrift für Harald H. Zimmermann. Herausgegeben von Ilse Harms, Heinz-Dirk Luckhardt und Hans W. Giessen

Authors

Years

Languages

  • e 21
  • d 10

Types

  • a 29
  • el 1
  • r 1
  • s 1
  • More… Less…

Themes