Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts

TL;DR

This paper introduces a hierarchical neural network model that leverages contextual information from surrounding sentences to improve sequential sentence classification in medical abstracts, outperforming previous methods.

Contribution

The study proposes a novel hierarchical sequential labeling network that effectively utilizes sentence context, achieving state-of-the-art results in medical abstract classification.

Findings

Model outperforms previous methods by 2-3%

Effective use of contextual sentence information

Improved classification accuracy on benchmark datasets

Abstract

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.