MedTextSeg: A Deep Dual Sequential Model for Section Segmentation in Medical Reports
Shaika Chowdhury, Halid Ziya Yerebakan, Yoshihisa Shinagawa, Philip S. Yu · 2021 IEEE International Conference on Big Data (Big Data) · 2021
Segmenting clinical texts into sections that uncover the underlying content structure is an important NLP task as it can facilitate robust information extraction in the medical domain. Recently, supervised neural models have been proposed for this task, however, they fail to capture the multi-granural sequential dependency characteristic within the texts effectively. In this light, we introduce the MedTextSeg model, which learns the sentence representation by modeling both the local and global contexts from the surrounding sentences. Through both quantitative and qualitative evaluations, we show that the proposed sequential encoding of sentences improves the overall section label prediction performance on several real-world datasets compared to state-of-the-art methods.