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  • × theme_ss:"Automatisches Abstracting"
  1. Endres-Niggemeyer, B.: Referierregeln und Referate : Abstracting als regelgesteuerter Textverarbeitungsprozeß (1985) 0.01 
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    Abstract
    Referierregeln steuern Referierprozesse. Inhaltsbezogene Vorschriften aus drei Referierregeln wurden mit zugehö-rigen Abstracts verglichen. Das Ergebnis war unbefriedi-gend: Referierregeln sind teilweise inkonsistent, ihre Angaben sind nicht immer sachgerecht und oft als Hand-lungsanleitung nicht geeignet. Referieren erscheint als unterbestimmter Denk- und Textverarbeitungsvorgang mit beachtlichem Klärungs- und Gestaltungsbedarf. Die Regeln enthalten zuwenig Wissen über die von ihnen geregelten Sachverhalte. Sie geben oft zu einfache und sachferne Inhaltsstrukturen vor. Ideen für differenziertere Referatstrukturen werden entwickelt. Sie berücksichtigen die Abhängigkeit der Referatstruktur von der Textstruktur des Originaldokuments stärker. Die Klärung des Referier-vorganges bis zu einer gemeinsamen Zieldefinition ist für die weitere Entwicklung des intellektuellen wie des automatischen Referierens wichtig.
  2. Yusuff, A.: Automatisches Indexing and Abstracting : Grundlagen und Beispiele (2002) 0.01 
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  3. Ruda, S.: Maschinenunterstützte Kondensierung von Fachtexten mit CONNY : Abstracting am Beispiel eines 'Nachrichten für Dokumentation'-Textkorpus (1994) 0.00 
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    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
  4. Haag, M.: Automatic text summarization (2002) 0.00 
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    Source
    Information - Wissenschaft und Praxis. 53(2002) H.4, 243-244
  5. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.00 
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    Date
    26. 2.1997 10:22:43
  6. Johnson, F.: Automatic abstracting research (1995) 0.00 
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    Abstract
    Discusses the attraction for researchers of the prospect of automatically generating abstracts but notes that the promise of superseding the human effort has yet to be realized. Notes ways in which progress in automatic abstracting research may come about and suggests a shift in the aim from reproducing the conventional benefits of abstracts to accentuating the advantages to users of the computerized representation of information in large textual databases
  7. Soricut, R.; Marcu, D.: Abstractive headline generation using WIDL-expressions (2007) 0.00 
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    Abstract
    We present a new paradigm for the automatic creation of document headlines that is based on direct transformation of relevant textual information into well-formed textual output. Starting from an input document, we automatically create compact representations of weighted finite sets of strings, called WIDL-expressions, which encode the most important topics in the document. A generic natural language generation engine performs the headline generation task, driven by both statistical knowledge encapsulated in WIDL-expressions (representing topic biases induced by the input document) and statistical knowledge encapsulated in language models (representing biases induced by the target language). Our evaluation shows similar performance in quality with a state-of-the-art, extractive approach to headline generation, and significant improvements in quality over previously proposed solutions to abstractive headline generation.
  8. Atanassova, I.; Bertin, M.; Larivière, V.: On the composition of scientific abstracts (2016) 0.00 
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    Abstract
    Purpose - Scientific abstracts reproduce only part of the information and the complexity of argumentation in a scientific article. The purpose of this paper provides a first analysis of the similarity between the text of scientific abstracts and the body of articles, using sentences as the basic textual unit. It contributes to the understanding of the structure of abstracts. Design/methodology/approach - Using sentence-based similarity metrics, the authors quantify the phenomenon of text re-use in abstracts and examine the positions of the sentences that are similar to sentences in abstracts in the introduction, methods, results and discussion structure, using a corpus of over 85,000 research articles published in the seven Public Library of Science journals. Findings - The authors provide evidence that 84 percent of abstract have at least one sentence in common with the body of the paper. Studying the distributions of sentences in the body of the articles that are re-used in abstracts, the authors show that there exists a strong relation between the rhetorical structure of articles and the zones that authors re-use when writing abstracts, with sentences mainly coming from the beginning of the introduction and the end of the conclusion. Originality/value - Scientific abstracts contain what is considered by the author(s) as information that best describe documents' content. This is a first study that examines the relation between the contents of abstracts and the rhetorical structure of scientific articles. The work might provide new insight for improving automatic abstracting tools as well as information retrieval approaches, in which text organization and structure are important features.
  9. Martinez-Romo, J.; Araujo, L.; Fernandez, A.D.: SemGraph : extracting keyphrases following a novel semantic graph-based approach (2016) 0.00 
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    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.
  10. Johnson, F.C.; Paice, C.D.; Black, W.J.; Neal, A.P.: ¬The application of linguistic processing to automatic abstract generation (1993) 0.00 
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.538-552.
  11. Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994) 0.00 
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.478-483.
  12. Marsh, E.: ¬A production rule system for message summarisation (1984) 0.00 
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.534-537.
  13. Advances in automatic text summarization (1999) 0.00 
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    Footnote
    Rez. in: Knowledge organization 27(2000) no.3, S.178-180 (H. Saggion)
  14. Ouyang, Y.; Li, W.; Li, S.; Lu, Q.: Intertopic information mining for query-based summarization (2010) 0.00 
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    Abstract
    In this article, the authors address the problem of sentence ranking in summarization. Although most existing summarization approaches are concerned with the information embodied in a particular topic (including a set of documents and an associated query) for sentence ranking, they propose a novel ranking approach that incorporates intertopic information mining. Intertopic information, in contrast to intratopic information, is able to reveal pairwise topic relationships and thus can be considered as the bridge across different topics. In this article, the intertopic information is used for transferring word importance learned from known topics to unknown topics under a learning-based summarization framework. To mine this information, the authors model the topic relationship by clustering all the words in both known and unknown topics according to various kinds of word conceptual labels, which indicate the roles of the words in the topic. Based on the mined relationships, we develop a probabilistic model using manually generated summaries provided for known topics to predict ranking scores for sentences in unknown topics. A series of experiments have been conducted on the Document Understanding Conference (DUC) 2006 data set. The evaluation results show that intertopic information is indeed effective for sentence ranking and the resultant summarization system performs comparably well to the best-performing DUC participating systems on the same data set.
  15. Craven, T.C.: Presentation of repeated phrases in a computer-assisted abstracting tool kit (2001) 0.00 
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  16. Bateman, J.; Teich, E.: Selective information presentation in an integrated publication system : an application of genre-driven text generation (1995) 0.00 
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  17. Edmundson, H.P.; Wyllis, R.E.: Problems in automatic abstracting (1964) 0.00 
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  18. Mani, T.: Automatic summarization (2001) 0.00 
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    Footnote
    Rez. in: JASIST 53(2002) no.5, S.410-411 (S.J. Lincicium)
  19. Moens, M.-F.: Summarizing court decisions (2007) 0.00 
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    Abstract
    In the field of law there is an absolute need for summarizing the texts of court decisions in order to make the content of the cases easily accessible for legal professionals. During the SALOMON and MOSAIC projects we investigated the summarization and retrieval of legal cases. This article presents some of the main findings while integrating the research results of experiments on legal document summarization by other research groups. In addition, we propose novel avenues of research for automatic text summarization, which we currently exploit when summarizing court decisions in the ACILA project. Techniques for automated concept learning and argument recognition are here the most challenging.
  20. Marcu, D.: Automatic abstracting and summarization (2009) 0.00 
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    Abstract
    After lying dormant for a few decades, the field of automated text summarization has experienced a tremendous resurgence of interest. Recently, many new algorithms and techniques have been proposed for identifying important information in single documents and document collections, and for mapping this information into grammatical, cohesive, and coherent abstracts. Since 1997, annual workshops, conferences, and large-scale comparative evaluations have provided a rich environment for exchanging ideas between researchers in Asia, Europe, and North America. This entry reviews the main developments in the field and provides a guiding map to those interested in understanding the strengths and weaknesses of an increasingly ubiquitous technology.

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