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  • × theme_ss:"Automatisches Abstracting"
  1. Wang, W.; Hwang, D.: Abstraction Assistant : an automatic text abstraction system (2010) 0.05 
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    Abstract
    In the interest of standardization and quality assurance, it is desirable for authors and staff of access services to follow the American National Standards Institute (ANSI) guidelines in preparing abstracts. Using the statistical approach an extraction system (the Abstraction Assistant) was developed to generate informative abstracts to meet the ANSI guidelines for structural content elements. The system performance is evaluated by comparing the system-generated abstracts with the author's original abstracts and the manually enhanced system abstracts on three criteria: balance (satisfaction of the ANSI standards), fluency (text coherence), and understandability (clarity). The results suggest that it is possible to use the system output directly without manual modification, but there are issues that need to be addressed in further studies to make the system a better tool.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.9, S.1790-1799
  2. Kuhlen, R.: Informationsaufbereitung III : Referieren (Abstracts - Abstracting - Grundlagen) (2004) 0.03 
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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
  3. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.03 
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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
  4. Saggion, H.; Lapalme, G.: Selective analysis for the automatic generation of summaries (2000) 0.02 
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    Abstract
    Selective Analysis is a new method for text summarization of technical articles whose design is based on the study of a corpus of professional abstracts and technical documents The method emphasizes the selection of particular types of information and its elaboration exploring the issue of dynamical summarization. A computer prototype was developed to demonstrate the viability of the approach and the automatic abstracts were evaluated using human informants. The results so far obtained indicate that the summaries are acceptable in content and text quality
    Series
    Advances in knowledge organization; vol.7
    Source
    Dynamism and stability in knowledge organization: Proceedings of the 6th International ISKO-Conference, 10-13 July 2000, Toronto, Canada. Ed.: C. Beghtol et al
  5. Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996) 0.02 
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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
  6. Endres-Niggemeyer, B.; Neugebauer, E.: Professional summarizing : no cognitive simulation without observation (1998) 0.02 
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    Abstract
    Develops a cognitive model of expert summarization, using 54 working processes of 6 experts recorded by thinking-alound protocols. It comprises up to 140 working steps. Components of the model are a toolbox of empirically founded strategies, principles of process organization, and interpreted working steps where the interaction of cognitive strategies can be investigated. In the computerized simulation the SimSum (Simulation of Summarizing) system, cognitive strategies are represented by object-oriented agents grouped around dedicated blckboards
    Source
    Journal of the American Society for Information Science. 49(1998) no.6, S.486-506
  7. Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007) 0.02 
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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
  8. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.02 
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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
  9. Atanassova, I.; Bertin, M.; Larivière, V.: On the composition of scientific abstracts (2016) 0.02 
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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.
  10. Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023) 0.02 
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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
  11. Wang, S.; Koopman, R.: Embed first, then predict (2019) 0.01 
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    Footnote
    Beitrag eines Special Issue: Research Information Systems and Science Classifications; including papers from "Trajectories for Research: Fathoming the Promise of the NARCIS Classification," 27-28 September 2018, The Hague, The Netherlands.
    Source
    Knowledge organization. 46(2019) no.5, S.364-370
  12. Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006) 0.01 
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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
  13. Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016) 0.01 
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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
  14. Advances in automatic text summarization (1999) 0.01 
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    Footnote
    Rez. in: Knowledge organization 27(2000) no.3, S.178-180 (H. Saggion)
  15. Bateman, J.; Teich, E.: Selective information presentation in an integrated publication system : an application of genre-driven text generation (1995) 0.01 
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    Source
    Information processing and management. 31(1995) no.5, S.753-767
  16. Pinto, M.: Engineering the production of meta-information : the abstracting concern (2003) 0.01 
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    Source
    Journal of information science. 29(2003) no.5, S.405-418
  17. McKeown, K.; Robin, J.; Kukich, K.: Generating concise natural language summaries (1995) 0.01 
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    Abstract
    Description of the problems for summary generation, the applications developed (for basket ball games - STREAK and for telephone network planning activity - PLANDOC), the linguistic constructions that the systems use to convey information concisely and the textual constraints that determine what information gets included
    Source
    Information processing and management. 31(1995) no.5, S.703-733
  18. Paice, C.D.: Automatic abstracting (1994) 0.01 
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    Source
    Encyclopedia of library and information science. Vol.53, [=Suppl.16]
  19. Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996) 0.01 
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    Date
    6. 3.1997 16:22:15
  20. Ouyang, Y.; Li, W.; Li, S.; Lu, Q.: Intertopic information mining for query-based summarization (2010) 0.01 
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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.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.1062-1072

Authors

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