Merge pull request #1 from WING-NUS/hugoblox-import-publications

Hugo Blox Builder - Import latest publications

content/publication/huang-etal-2022-lightweight/cite.bib

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+@inproceedings{huang-etal-2022-lightweight,
+ abstract = {Logical structure recovery in scientific articles associates text with a semantic section of the article. Although previous work has disregarded the surrounding context of a line, we model this important information by employing line-level attention on top of a transformer-based scientific document processing pipeline. With the addition of loss function engineering and data augmentation techniques with semi-supervised learning, our method improves classification performance by 10% compared to a recent state-of-the-art model. Our parsimonious, text-only method achieves a performance comparable to that of other works that use rich document features such as font and spatial position, using less data without sacrificing performance, resulting in a lightweight training pipeline.},
+ address = {Gyeongju, Republic of Korea},
+ author = {Huang, Po-Wei  and
+Ramesh Kashyap, Abhinav  and
+Qin, Yanxia  and
+Yang, Yajing  and
+Kan, Min-Yen},
+ booktitle = {Proceedings of the Third Workshop on Scholarly Document Processing},
+ editor = {Cohan, Arman  and
+Feigenblat, Guy  and
+Freitag, Dayne  and
+Ghosal, Tirthankar  and
+Herrmannova, Drahomira  and
+Knoth, Petr  and
+Lo, Kyle  and
+Mayr, Philipp  and
+Shmueli-Scheuer, Michal  and
+de Waard, Anita  and
+Wang, Lucy Lu},
+ month = {October},
+ pages = {37--48},
+ publisher = {Association for Computational Linguistics},
+ title = {Lightweight Contextual Logical Structure Recovery},
+ url = {https://aclanthology.org/2022.sdp-1.5},
+ year = {2022}
+}

content/publication/huang-etal-2022-lightweight/index.md

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+---
+title: Lightweight Contextual Logical Structure Recovery
+authors:
+- Po-Wei Huang
+- Abhinav Ramesh Kashyap
+- Yanxia Qin
+- Yajing Yang
+- Min-Yen Kan
+date: '2022-10-01'
+publishDate: '2024-07-05T10:15:26.841390Z'
+publication_types:
+- paper-conference
+publication: '*Proceedings of the Third Workshop on Scholarly Document Processing*'
+abstract: Logical structure recovery in scientific articles associates text with a
+  semantic section of the article. Although previous work has disregarded the surrounding
+  context of a line, we model this important information by employing line-level attention
+  on top of a transformer-based scientific document processing pipeline. With the
+  addition of loss function engineering and data augmentation techniques with semi-supervised
+  learning, our method improves classification performance by 10% compared to a recent
+  state-of-the-art model. Our parsimonious, text-only method achieves a performance
+  comparable to that of other works that use rich document features such as font and
+  spatial position, using less data without sacrificing performance, resulting in
+  a lightweight training pipeline.
+links:
+- name: URL
+  url: https://aclanthology.org/2022.sdp-1.5
+---