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How Many Pages? Paper Length Prediction from the Metadata

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10424516" target="_blank" >RIV/00216208:11320/20:10424516 - isvavai.cz</a>

  • Result on the web

    <a href="https://dl.acm.org/doi/10.1145/3443279.3443305" target="_blank" >https://dl.acm.org/doi/10.1145/3443279.3443305</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1145/3443279.3443305" target="_blank" >10.1145/3443279.3443305</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    How Many Pages? Paper Length Prediction from the Metadata

  • Original language description

    Being able to predict the length of a scientific paper may be helpful in numerous situations. This work defines the paper length prediction task as a regression problem and reports several experimental results using popular machine learning models. We also create a huge dataset of publication metadata and the respective lengths in number of pages. The dataset will be freely available and is intended to foster research in this domain. As future work, we would like to explore more advanced regressors based on neural networks and big pretrained language models.

  • 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

    <a href="/en/project/GX19-26934X" target="_blank" >GX19-26934X: Neural Representations in Multi-modal and Multi-lingual Modeling</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2020

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    4th International Conference on Natural Language Processing and Information Retrieval

  • ISBN

    978-1-4503-7760-7

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    91-95

  • Publisher name

    ACM

  • Place of publication

    New York, USA

  • Event location

    Seoul, Korea

  • Event date

    Dec 18, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article