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SumeCzech: Large Czech News-Based Summarization Dataset

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10390209" target="_blank" >RIV/00216208:11320/18:10390209 - isvavai.cz</a>

  • Result on the web

    <a href="http://www.lrec-conf.org/proceedings/lrec2018/summaries/825.html" target="_blank" >http://www.lrec-conf.org/proceedings/lrec2018/summaries/825.html</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    SumeCzech: Large Czech News-Based Summarization Dataset

  • Original language description

    Document summarization is a well-studied NLP task. With the emergence of artificial neural network models, the summarization performance is increasing, as are the requirements on training data. However, only a few datasets are available for Czech, none of them particularly large. Additionally, summarization has been evaluated predominantly on English, with the commonly used ROUGE metric being English-specific. In this paper, we try to address both issues. We present SumeCzech, a Czech news-based summarization dataset. It contains more than a million documents, each consisting of a headline, a several sentences long abstract and a full text. The dataset can be downloaded using the provided scripts available at http://hdl.handle.net/11234/1-2615. We evaluate several summarization baselines on the dataset, including a strong abstractive approach based on Transformer neural network architecture. The evaluation is performed using a language-agnostic variant of ROUGE.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC 2018)

  • ISBN

    979-10-95546-00-9

  • ISSN

  • e-ISSN

    neuvedeno

  • Number of pages

    8

  • Pages from-to

    3488-3495

  • Publisher name

    European Language Resources Association

  • Place of publication

    Paris, France

  • Event location

    Miyazaki, Japan

  • Event date

    May 7, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article