# Neural Networks for Joint Sentence Classification in Medical Paper Abstracts

arXiv: https://arxiv.org/abs/1612.05251 · HTML: https://mlsanity.com/paper/1612.05251/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2016.

## Built on by (most cited first, 10 of 10)

- [Cross-Domain Multi-Task Learning for Sequential Sentence Classification in Research Papers](https://mlsanity.com/paper/2102.06008/) (2021)
- [Self-supervised Regularization for Text Classification](https://mlsanity.com/paper/2103.05231/) (2021)
- [Multi-label Sequential Sentence Classification via Large Language Model](https://mlsanity.com/paper/2411.15623/) (2024)
- [Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature](https://mlsanity.com/paper/2412.11125/) (2024)
- [Sectioning of Biomedical Abstracts: A Sequence of Sequence Classification Task](https://mlsanity.com/paper/2201.07112/) (2022)
- [Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge](https://mlsanity.com/paper/2305.03287/) (2023)
- [Coupling Local Context and Global Semantic Prototypes via a Hierarchical Architecture for Rhetorical Roles Labeling](https://mlsanity.com/paper/2603.03856/) (2026)
- [LSTM-based Deep Neural Network With A Focus on Sentence Representation for Sequential Sentence Classification in Medical Scientific Abstracts](https://mlsanity.com/paper/2401.15854/) (2024)
- [Constructing the CORD-19 Vaccine Dataset](https://mlsanity.com/paper/2407.18471/) (2024)
- [Extracting Problem and Method Sentence from Scientific Papers: A Context-enhanced Transformer Using Formulaic Expression Desensitization](https://mlsanity.com/paper/2606.26481/) (2026)

## Abstract (arXiv)

Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually. However, traditional sentence classification approaches have been shown to greatly benefit from jointly classifying subsequent sentences, such as with conditional random fields. In this work, we present an ANN architecture that combines the effectiveness of typical ANN models to classify sentences in isolation, with the strength of structured prediction. Our model achieves state-of-the-art results on two different datasets for sequential sentence classification in medical abstracts.