Sectioning of Biomedical Abstracts: A Sequence of Sequence Classification Task

TL;DR

This paper investigates deep learning models for classifying sections of biomedical abstracts, focusing on the SSN-4 model's performance, generalization, and the impact of task-specific word embeddings.

Contribution

It evaluates the SSN-4 model's effectiveness, explores component trade-offs, and examines generalization to new datasets in biomedical abstract section classification.

Findings

SSN-4 model performs well on RCT dataset

Model does not generalize effectively beyond RCT data

Task-specific word embeddings can influence performance

Abstract

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.