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VaryLaTeX: Learning Paper Variants That Meet Constraints

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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Submitted on : Tuesday, December 12, 2017 - 6:46:27 PM
Last modification on : Tuesday, January 4, 2022 - 3:50:51 AM


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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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