Due to digitalization and increasing dependency on the computers, an enormous amount of data is being produced every day. This data contains very useful information, which would get wasted if not managed properly. Hence, the topic of automatic text summarization has become very essential to extract useful information without wasting the important resources. Various papers based on different techniques of text Sumz have been surveyed and mentioned in this paper. On the basis of input, these techniques are broadly divided into two types: Single document and multi documents and on the basis of output or the summary generated as Abstractive and Extractive Sumz. Some of the datasets used by the authors were English Gigawords, LCSTS, CNN daily mails, DUC-2002 etc. This study aims to give a brief overview of the developments in the field of Automatic Text Sumz in the last couple of years on the basis of various parameters.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Review on Various Techniques of Automatic Text Summarization


    Beteiligte:
    Gupta, Aaryan (Autor:in) / Rahul (Autor:in) / Khatri, Inder (Autor:in) / Monika (Autor:in)


    Erscheinungsdatum :

    05.11.2020


    Format / Umfang :

    918421 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Deep recurrent neural networks for abstractive text summarization

    Klönne, Marie | Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2018

    Freier Zugriff


    Automatic Video Summarization by Graph Modeling

    Ngo, C. / Ma, Y. / Zhang, H. et al. | British Library Conference Proceedings | 2003


    Automatic video summarization by graph modeling

    Chong-Wah Ngo, / Yu-Fei Ma, / Hong-Jiang Zhang, | IEEE | 2003