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.

医学摘要中的连续句子分类任务（SSC）使用多级深度学习模型，包括 LSTM-based 网络和分层结构，来提取句子嵌入，进而实现更好的分类性能。

基于 LSTM 的深度神经网络，关注于句子表示，用于医学科学摘要中的序列句子分类

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

Rapid growth of the biomedical literature has led to many advances in the
biomedical text mining field. Among the vast amount of information, biomedical
article abstracts are the easily accessible sources. However, the number of the
structured abstracts, describing the rhetorical sections with one of
Background, Objective, Method, Result and Conclusion categories is still not
considerable. Exploration of valuable information in the biomedical abstracts
can be expedited with the improvements in the sequential sentence
classification task. Deep learning based models has great performance/potential
in achieving significant results in this task. However, they can often be
overly complex and overfit to specific data. In this project, we study a
state-of-the-art deep learning model, which we called SSN-4 model here. We
investigate different components of the SSN-4 model to study the trade-off
between the performance and complexity. We explore how well this model
generalizes to a new data set beyond Randomized Controlled Trials (RCT)
dataset. We address the question that whether word embeddings can be adjusted
to the task to improve the performance. Furthermore, we develop a second model
that addresses the confusion pairs in the first model. Results show that SSN-4
model does not appear to generalize well beyond RCT dataset.

本研究基于最先进的深度学习模型（SSN-4 模型），探讨了不同的模型组件对表现和复杂性之间的平衡，同时调整单词嵌入来提高性能，并提出了一个解决第一个模型中混淆的对的第二个模型。结果表明，SSN-4 模型似乎不适用于 RCT 数据集之外。

医学摘要的分类分节：一系列序列分类任务

Sectioning of Biomedical Abstracts: A Sequence of Sequence Classification Task

We present PubMed 200k RCT, a new dataset based on PubMed for sequential
sentence classification. The dataset consists of approximately 200,000
abstracts of randomized controlled trials, totaling 2.3 million sentences. Each
sentence of each abstract is labeled with their role in the abstract using one
of the following classes: background, objective, method, result, or conclusion.
The purpose of releasing this dataset is twofold. First, the majority of
datasets for sequential short-text classification (i.e., classification of
short texts that appear in sequences) are small: we hope that releasing a new
large dataset will help develop more accurate algorithms for this task. Second,
from an application perspective, researchers need better tools to efficiently
skim through the literature. Automatically classifying each sentence in an
abstract would help researchers read abstracts more efficiently, especially in
fields where abstracts may be long, such as the medical field.

本文介绍了 PubMed 200k RCT 数据集，该数据集基于 PubMed 用于序列句子分类，旨在为开发更准确的算法和在文献阅读方面提供更好的工具。