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VaryLaTeX: Learning Paper Variants That Meet Constraints

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Mathieu Acher
Paul Temple
  • Function : Author
  • PersonId : 16528
  • IdHAL : paul-temple
José Ángel Galindo Duarte
  • Function : Author
  • PersonId : 783265
  • IdRef : 187874530
Jabier Martinez
  • Function : Author
  • PersonId : 971500
Tewfik Ziadi

Abstract

How to submit a research paper, a technical report, a grant proposal , or a curriculum vitae that respect imposed constraints such as formatting instructions and page limits? It is a challenging task, especially when coping with time pressure. In this work, we present VaryL A T E X, a solution based on variability, constraint programming , and machine learning techniques for documents written in L A T E X to meet constraints and deliver on time. Users simply have to annotate L A T E X source files with variability information, e.g., (de)activating portions of text, tuning figures' sizes, or tweaking line spacing. Then, a fully automated procedure learns constraints among Boolean and numerical values for avoiding non-acceptable paper variants, and finally, users can further configure their papers (e.g., aesthetic considerations) or pick a (random) paper variant that meets constraints, e.g., page limits. We describe our implementation and report the results of two experiences with VaryL A T E X.
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Dates and versions

hal-01659161 , version 1 (12-12-2017)

Identifiers

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Mathieu Acher, Paul Temple, Jean-Marc Jézéquel, José Ángel Galindo Duarte, Jabier Martinez, et al.. VaryLaTeX: Learning Paper Variants That Meet Constraints. VaMoS 2018 - 12th International Workshop on Variability Modelling of Software-Intensive Systems, Feb 2018, Madrid, Spain. pp.83-88, ⟨10.1145/3168365.3168372⟩. ⟨hal-01659161⟩
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