LSTM-based Deep Neural Network With A Focus on Sentence Representation
  for Sequential Sentence Classification in Medical Scientific Abstracts
Phat Lam Lam Pham Tin Nguyen Hieu Tang Seidl Michael Alexander Schindler
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 often sequentially related to each
other. For this reason, the role of sentence embedding is crucial for capturing
both the semantic information between words in the sentence and the contextual
relationship of sentences within the abstract to provide a comprehensive
representation for better classification. In this paper, we present a
hierarchical deep learning model for the SSC task. First, we propose a
LSTM-based network with multiple feature branches to create well-presented
sentence embeddings at the sentence level. To perform the sequence of
sentences, a convolutional-recurrent neural network (C-RNN) at the abstract
level and a multi-layer perception network (MLP) at the segment level are
developed that further enhance the model performance. Additionally, an ablation
study is also conducted to evaluate the contribution of individual component in
the entire network to the model performance at different levels. Our proposed
system is very competitive to the state-of-the-art systems and further improve
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.