# Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts

arXiv: https://arxiv.org/abs/1808.06161 · HTML: https://mlsanity.com/paper/1808.06161/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2018.

## Built on by (most cited first, 22 of 22)

- [AI vs. Human -- Differentiation Analysis of Scientific Content Generation](https://mlsanity.com/paper/2301.10416/) (2023)
- [X-SCITLDR: Cross-Lingual Extreme Summarization of Scholarly Documents](https://mlsanity.com/paper/2205.15051/) (2022)
- [Cross-Domain Multi-Task Learning for Sequential Sentence Classification in Research Papers](https://mlsanity.com/paper/2102.06008/) (2021)
- [STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarking](https://mlsanity.com/paper/2507.03674/) (2025)
- [Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas](https://mlsanity.com/paper/2102.09761/) (2021)
- [Understand Legal Documents with Contextualized Large Language Models](https://mlsanity.com/paper/2303.12135/) (2023)
- [Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents](https://mlsanity.com/paper/2302.06448/) (2023)
- [Enhancing Pre-Trained Language Models with Sentence Position Embeddings for Rhetorical Roles Recognition in Legal Opinions](https://mlsanity.com/paper/2310.05276/) (2023)
- [Multi-label Sequential Sentence Classification via Large Language Model](https://mlsanity.com/paper/2411.15623/) (2024)
- [FLeW: Facet-Level and Adaptive Weighted Representation Learning of Scientific Documents](https://mlsanity.com/paper/2509.07531/) (2025)
- [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)
- [SPACE-IDEAS: A Dataset for Salient Information Detection in Space Innovation](https://mlsanity.com/paper/2403.16941/) (2024)
- [Classification and Clustering of Sentence-Level Embeddings of Scientific Articles Generated by Contrastive Learning](https://mlsanity.com/paper/2404.00224/) (2024)
- [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)
- [Detection of fields of applications in biomedical abstracts with the support of argumentation elements](https://mlsanity.com/paper/2404.06121/) (2024)
- [Pointer-Guided Pre-Training: Infusing Large Language Models with Paragraph-Level Contextual Awareness](https://mlsanity.com/paper/2406.04156/) (2024)
- [Black-Box Segmentation of Electronic Medical Records](https://mlsanity.com/paper/2409.19796/) (2024)
- [Enhancing Science Classroom Discourse Analysis through Joint Multi-Task Learning for Reasoning-Component Classification](https://mlsanity.com/paper/2604.21137/) (2026)
- [Extracting Problem and Method Sentence from Scientific Papers: A Context-enhanced Transformer Using Formulaic Expression Desensitization](https://mlsanity.com/paper/2606.26481/) (2026)
- [Improving Cross-Lingual Transfer for Sequential Sentence Classification in Research Papers via Structural Similarity](https://mlsanity.com/paper/2609.19650/) (2026)

## Abstract (arXiv)

Prevalent models based on artificial neural network (ANN) for sentence classification often classify sentences in isolation without considering the context in which sentences appear. This hampers the traditional sentence classification approaches to the problem of sequential sentence classification, where structured prediction is needed for better overall classification performance. In this work, we present a hierarchical sequential labeling network to make use of the contextual information within surrounding sentences to help classify the current sentence. Our model outperforms the state-of-the-art results by 2%-3% on two benchmarking datasets for sequential sentence classification in medical scientific abstracts.