An efficient information retrieval and tracking algorithm for multiple typesetting styles of the same paper
Academic paper publishers like IEEE, Elsevier, and Springer often require manuscripts to be typeset according to their own templates before they are submitted. Most journals and conferences make final and binding decisions, where the decisions for the last time cannot be discussed or changed again in the future unless a new manuscript or a revised manuscript whose revision rate exceeds a certain large percentage is submitted. This makes authors have to change the typesetting of their rejected manuscripts on most occasions before transferring their manuscripts to a more suitable place. Unfortunately, when either the typesetting style or the manuscript content needs to be changed or updated, it is very inconvenient and time-consuming for the traditional typesetting method to re-format the manuscript since it is cumbersome to manage multiple versions of LaTeX folders. Although online platforms like Overleaf support multiplayer online collaboration, they cannot fundamentally solve the problem of generating several manuscripts of different typesetting styles with the same content by updating the manuscript content only once. To make it more flexible to change the typesetting styles, we aim to provide a novel typesetting method that supports multiple typesetting styles of the same manuscript at the same time. The manuscript content and the typesetting controlling parts are entirely separated. Meanwhile, the grammar detection and structure tracking of LaTeX source codes are presented in a way that corresponds to the new typesetting method, which can provide more appropriate results. Compared with the traditional method, the proposed typesetting method can efficiently generate multiple typesetting styles for the same manuscript by reducing the time complexity from O(mn) to O(m) on m updates with n typesetting templates, whereas the proposed grammar detection and structure tracking algorithm can be performed more efficiently and correctly.
Authors
- Tong Ji (ORCID: https://orcid.org/0000-0002-5119-0305)
- Yubing Wu (ORCID: https://orcid.org/0000-0002-9456-600X)
- Yifeng Lin (ORCID: https://orcid.org/0000-0001-6983-6162)
- Jiyue Tang
- Yuer Yang (ORCID: https://orcid.org/0000-0001-6489-286X)
- Yuan Li (ORCID: https://orcid.org/0000-0002-4462-6764)
- Xiaoyu Xu (ORCID: https://orcid.org/0000-0002-9130-9657)
- Yuxin Luo (ORCID: https://orcid.org/0000-0003-1767-6069)
- Yingying Liu (ORCID: https://orcid.org/0000-0002-6792-8933)
Institutions
- Jinan University (CN)
- Nanyang Technological University (SG)
- University of Hong Kong (HK)
Publication Details
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1109/dsit61374.2024.10880935
- Primary Topic
- Web Data Mining and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00