LSTM-based Deep Neural Network With A Focus on Sentence Representation for Sequential Sentence Classification in Medical Scientific Abstracts

TL;DR

This paper introduces an LSTM-based neural network focusing on detailed sentence representations to improve sequential sentence classification in medical abstracts, achieving higher F1 scores on benchmark datasets.

Contribution

It presents a novel LSTM-based sentence embedding method and a combined neural system that significantly enhances classification performance in medical abstract sentence categorization.

Findings

Achieved up to 2.8% F1 score improvement on benchmark datasets.

Demonstrated the importance of comprehensive sentence representations.

System outperforms state-of-the-art methods.

Abstract

The Sequential Sentence Classification task within the domain of medical abstracts, termed as SSC, involves the categorization of sentences into pre-defined headings based on their roles in conveying critical information in the abstract. In the SSC task, sentences are sequentially related to each other. For this reason, the role of sentence embeddings is crucial for capturing both the semantic information between words in the sentence and the contextual relationship of sentences within the abstract, which then enhances the SSC system performance. In this paper, we propose a LSTM-based deep learning network with a focus on creating comprehensive sentence representation at the sentence level. To demonstrate the efficacy of the created sentence representation, a system utilizing these sentence embeddings is also developed, which consists of a Convolutional-Recurrent neural network (C-RNN) at the abstract level and a multi-layer perception network (MLP) at the segment level. Our proposed system yields highly competitive results compared to state-of-the-art systems and further enhances the F1 scores of the baseline by 1.0%, 2.8%, and 2.6% on the benchmark datasets PudMed 200K RCT, PudMed 20K RCT and NICTA-PIBOSO, respectively. This indicates the significant impact of improving sentence representation on boosting model performance.