Identification of semantic subheadings in biomedical article abstracts helps individuals find relevant literature at a faster pace. In this work, we propose a new model that uses pointer network for abstract sectioning task.The proposed approach successfully integrates a pointer network into a transformer-based language model and outperforms all the previous SOTA models in both the RCT and GMED data sets.
GMED dataset can be downloaded at https://drive.google.com/file/d/1k-0shdcBf6lGVGSEaLqydgXq0joGf3YQ/view?usp=sharing
Source files and the trained model will be provided soon.