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

Sectioning of Biomedical Abstracts: A Sequence of Sequence Classification Task