Abstract
This research in progress paper introduces a novel academic abstract sentence classification system intended to improve researcher literature discovery efficiency. The system provides three key functions: 1) displays abstracts with visual identification of each sentence’s indicated literature characteristic class, 2) conversion of unstructured abstracts into structured variants and 3) categorised class sentence extraction available for export to CSV alongside literature metadata. This functionality is made possible by a web application connected to a Python instance via PHP, integration with an open access literature index via an API and a deployed academic abstract sentence classification model. The contribution of the proposed system is its ability to enhance researcher literature discovery. This paper provides context and motivation behind the development of the system, outlines its functionality and provides an outlook for future research.
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The authors wish to acknowledge the Australian Government Research Training Program Scholarship which enabled this research to take place.
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Stead, C., Smith, S., Busch, P., Vatanasakdakul, S. (2020). Towards an Academic Abstract Sentence Classification System. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_39
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