# Enhancing Identification of Structure Function of Academic Articles Using Contextual Information

arXiv: https://arxiv.org/abs/2111.14110 · HTML: https://mlsanity.com/paper/2111.14110/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 52 references that are in the map.
- First posted on arXiv 28 Nov 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Measuring academic influence: Not all citations are equal](https://mlsanity.com/paper/1501.06587/) (2015)
- [Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation](https://mlsanity.com/paper/1602.04874/) (2016)
- [Medical Theses and Derivative Articles: Dissemination Of Contents and Publication Patterns](https://mlsanity.com/paper/1707.04439/) (2017)

## Built on by (most cited first, 7 of 7)

- [SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers](https://mlsanity.com/paper/2505.16330/) (2025)
- [Which structure of academic articles do referees pay more attention to?: perspective of peer review and full-text of academic articles](https://mlsanity.com/paper/2209.01841/) (2022)
- [Look Ahead Text Understanding and LLM Stitching](https://mlsanity.com/paper/2412.17836/) (2024)
- [Enhancing Keyphrase Extraction from Academic Articles Using Section Structure Information](https://mlsanity.com/paper/2505.14149/) (2025)
- [Enhancing Abstractive Summarization of Scientific Papers Using Structure Information](https://mlsanity.com/paper/2505.14179/) (2025)
- [Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives](https://mlsanity.com/paper/2506.12242/) (2025)
- [Which Sections of a Research Paper Best Reveal Its Research Methods? Evidence from Library and Information Science](https://mlsanity.com/paper/2606.19051/) (2026)

## Abstract (arXiv)

With the enrichment of literature resources, researchers are facing the growing problem of information explosion and knowledge overload. To help scholars retrieve literature and acquire knowledge successfully, clarifying the semantic structure of the content in academic literature has become the essential research question. In the research on identifying the structure function of chapters in academic articles, only a few studies used the deep learning model and explored the optimization for feature input. This limits the application, optimization potential of deep learning models for the research task. This paper took articles of the ACL conference as the corpus. We employ the traditional machine learning models and deep learning models to construct the classifiers based on various feature input. Experimental results show that (1) Compared with the chapter content, the chapter title is more conducive to identifying the structure function of academic articles. (2) Relative position is a valuable feature for building traditional models. (3) Inspired by (2), this paper further introduces contextual information into the deep learning models and achieved significant results. Meanwhile, our models show good migration ability in the open test containing 200 sampled non-training samples. We also annotated the ACL main conference papers in recent five years based on the best practice performing models and performed a time series analysis of the overall corpus. This work explores and summarizes the practical features and models for this task through multiple comparative experiments and provides a reference for related text classification tasks. Finally, we indicate the limitations and shortcomings of the current model and the direction of further optimization.