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  • × theme_ss:"Automatisches Abstracting"
  1. Kuhlen, R.: In Richtung Summarizing für Diskurse in K3 (2006) 0.05 
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    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
  2. Hahn, U.: Automatisches Abstracting (2013) 0.02 
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    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
  3. Endres-Niggemeyer, B.: ¬An empirical process model of abstracting (1992) 0.02 
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    Series
    Schriften zur Informationswissenschaft; Bd.7
    Source
    Mensch und Maschine: Informationelle Schnittstellen der Kommunikation. Proc. des 3. Int. Symposiums für Informationswissenschaft (ISI'92), 5.-7.11.1992 in Saarbrücken. Hrsg.: H.H. Zimmermann, H.-D. Luckhardt u. A. Schulz
  4. Hahn, U.: ¬Die Verdichtung textuellen Wissens zu Information : vom Wandel methodischer Paradigmen beim automatischen Abstracting (2004) 0.01 
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    Series
    Schriften zur Informationswissenschaft; Bd.41
    Source
    Wissen in Aktion: Der Primat der Pragmatik als Motto der Konstanzer Informationswissenschaft. Festschrift für Rainer Kuhlen. Hrsg. R. Hammwöhner, u.a
  5. Kuhlen, R.: Informationsaufbereitung III : Referieren (Abstracts - Abstracting - Grundlagen) (2004) 0.01 
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    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
  6. Kuhlen, R.: Abstracts, abstracting : intellektuelle und maschinelle Verfahren (1990) 0.01 
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    Source
    Grundlagen der praktischen Information und Dokumentation. 3. Aufl. Hrsg.: M. Buder u.a. Bd.1
  7. Kuhlen, R.: Abstracts, abstracting : intellektuelle und maschinelle Verfahren (1997) 0.00 
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    Source
    Grundlagen der praktischen Information und Dokumentation: ein Handbuch zur Einführung in die fachliche Informationsarbeit. 4. Aufl. Hrsg.: M. Buder u.a
  8. Endres-Niggemeyer, B.; Jauris-Heipke, S.; Pinsky, S.M.; Ulbricht, U.: Wissen gewinnen durch Wissen : Ontologiebasierte Informationsextraktion (2006) 0.00 
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    Abstract
    Die ontologiebasierte Informationsextraktion, über die hier berichtet wird, ist Teil eines Systems zum automatischen Zusammenfassen, das sich am Vorgehen kompetenter Menschen orientiert. Dahinter steht die Annahme, dass Menschen die Ergebnisse eines Systems leichter übernehmen können, wenn sie mit Verfahren erarbeitet worden sind, die sie selbst auch benutzen. Das erste Anwendungsgebiet ist Knochenmarktransplantation (KMT). Im Kern des Systems Summit-BMT (Summarize It in Bone Marrow Transplantation) steht eine Ontologie des Fachgebietes. Sie ist als MySQL-Datenbank realisiert und versorgt menschliche Benutzer und Systemkomponenten mit Wissen. Summit-BMT unterstützt die Frageformulierung mit einem empirisch fundierten Szenario-Interface. Die Retrievalergebnisse werden durch ein Textpassagenretrieval vorselektiert und dann kognitiv fundierten Agenten unterbreitet, die unter Einsatz ihrer Wissensbasis / Ontologie genauer prüfen, ob die Propositionen aus der Benutzerfrage getroffen werden. Die relevanten Textclips aus dem Duelldokument werden in das Szenarioformular eingetragen und mit einem Link zu ihrem Vorkommen im Original präsentiert. In diesem Artikel stehen die Ontologie und ihr Gebrauch zur wissensbasierten Informationsextraktion im Mittelpunkt. Die Ontologiedatenbank hält unterschiedliche Wissenstypen so bereit, dass sie leicht kombiniert werden können: Konzepte, Propositionen und ihre syntaktisch-semantischen Schemata, Unifikatoren, Paraphrasen und Definitionen von Frage-Szenarios. Auf sie stützen sich die Systemagenten, welche von Menschen adaptierte Zusammenfassungsstrategien ausführen. Mängel in anderen Verarbeitungsschritten führen zu Verlusten, aber die eigentliche Qualität der Ergebnisse steht und fällt mit der Qualität der Ontologie. Erste Tests der Extraktionsleistung fallen verblüffend positiv aus.
    Source
    Information - Wissenschaft und Praxis. 57(2006) H.6/7, S.301-308
  9. Endres-Niggemeyer, B.: Bessere Information durch Zusammenfassen aus dem WWW (1999) 0.00 
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    Abstract
    Am Beispiel der Knochenmarktransplantation, eines medizinischen Spezialgebietes, wird im folgenden dargelegt, wie man BenutzerInnen eine großen Teil des Aufwandes bei der Wissensbeschaffung abnehmen kann, indem man Suchergebnisse aus dem Netz fragebezogen zusammenfaßt. Dadurch wird in zeitkritischen Situationen, wie sie in Diagnose und Therapie alltäglich sind, die Aufnahme neuen Wissens ermöglicht. Auf einen Überblick über den Stand des Textzusammenfassens und der Ontologieentwicklung folgt eine Systemskizze, in der die Informationssuche im WWW durch ein kognitiv fundiertes Zusammenfassungssystem ergänzt wird. Dazu wird eine Fach-Ontologie vorgeschlagen, die das benötigte Wissen organisiert und repräsentiert.
  10. Haag, M.: Automatic text summarization (2002) 0.00 
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    Source
    Information - Wissenschaft und Praxis. 53(2002) H.4, 243-244
  11. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.00 
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    Abstract
    With the onset of the information explosion arising from digital libraries and access to a wealth of information through the Internet, the need to efficiently determine the relevance of a document becomes even more urgent. Describes a text extraction system (TES), which retrieves a set of sentences from a document to form an indicative abstract. Such an automated process enables information to be filtered more quickly. Discusses the combination of various text extraction techniques. Compares results with manually produced abstracts
    Date
    26. 2.1997 10:22:43
    Source
    Microcomputers for information management. 13(1996) no.1, S.41-55
  12. Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996) 0.00 
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    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
  13. Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007) 0.00 
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    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.
    Source
    Information processing and management. 43(2007) no.6, S.1606-1618
  14. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.00 
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    Abstract
    With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.
    Date
    22. 6.2023 14:55:20
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.7, S.759-774
  15. Haag, M.: Automatic text summarization : Evaluation des Copernic Summarizer und mögliche Einsatzfelder in der Fachinformation der DaimlerCrysler AG (2002) 0.00 
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    Abstract
    An evaluation of the Copernic Summarizer, a software for automatically summarizing text in various data formats, is being presented. It shall be assessed if and how the Copernic Summarizer can reasonably be used in the DaimlerChrysler Information Division in order to enhance the quality of its information services. First, an introduction into Automatic Text Summarization is given and the Copernic Summarizer is being presented. Various methods for evaluating Automatic Text Summarization systems and software ergonomics are presented. Two evaluation forms are developed with which the employees of the Information Division shall evaluate the quality and relevance of the extracted keywords and summaries as well as the software's usability. The quality and relevance assessment is done by comparing the original text to the summaries. Finally, a recommendation is given concerning the use of the Copernic Summarizer.
  16. Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023) 0.00 
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    Abstract
    The automatic summarization of scientific articles differs from other text genres because of the structured format and longer text length. Previous approaches have focused on tackling the lengthy nature of scientific articles, aiming to improve the computational efficiency of summarizing long text using a flat, unstructured abstract. However, the structured format of scientific articles and characteristics of each section have not been fully explored, despite their importance. The lack of a sufficient investigation and discussion of various characteristics for each section and their influence on summarization results has hindered the practical use of automatic summarization for scientific articles. To provide a balanced abstract proportionally emphasizing each section of a scientific article, the community introduced the structured abstract, an abstract with distinct, labeled sections. Using this information, in this study, we aim to understand tasks ranging from data preparation to model evaluation from diverse viewpoints. Specifically, we provide a preprocessed large-scale dataset and propose a summarization method applying the introduction, methods, results, and discussion (IMRaD) format reflecting the characteristics of each section. We also discuss the objective benchmarks and perspectives of state-of-the-art algorithms and present the challenges and research directions in this area.
    Date
    22. 1.2023 18:57:12
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.2, S.234-248
  17. Ruda, S.: Abstracting: eine Auswahlbibliographie (1992) 0.00 
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    Abstract
    Die vorliegende Auswahlbibliographie ist in 9 Themenbereiche unterteilt. Der erste Abschnitt enthält Literatur, in der auf Abstracts und Abstracting-Verfahren allgemein eingegangen und ein Überblick über den Stand der Forschung gegeben wird. Im nächsten Abschnitt werden solche Aufsätze referiert, die die historische Entwicklung des Abstracting beschreiben. Im dritten Teil sind Abstracting-Richtlinien verschiedener Institutionen aufgelistet. Lexikalische, syntaktische und semantische Textkondensierungsverfahren sind das Thema der in Abschnitt 4 präsentierten Arbeiten. Textstrukturen von Abstracts werden unter Punkt 5 betrachtet, und die Arbeiten des nächsten Themenbereiches befassen sich mit dem Problem des Schreibens von Abstracts. Der siebte Abschnitt listet sog. 'maschinelle' und maschinen-unterstützte Abstracting-Methoden auf. Anschließend werden 'maschinelle' und maschinenunterstützte Abstracting-Verfahren, Abstracts im Vergleich zu ihren Primärtexten sowie Abstracts im allgemeien bewertet. Den Abschluß bilden Bibliographien
  18. Endres-Niggemeyer, B.: Referierregeln und Referate : Abstracting als regelgesteuerter Textverarbeitungsprozeß (1985) 0.00 
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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.
  19. Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006) 0.00 
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    Date
    22. 7.2006 17:25:48
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.6, S.740-752
  20. Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016) 0.00 
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    Date
    22. 1.2016 12:29:41
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.2, S.366-379

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