Abstract
The classification of abstract sentences is a valuable tool to support scientific database querying, to summarize relevant literature works and to assist in the writing of new abstracts. This study proposes a novel deep learning approach based on a convolutional layer and a bi-directional gated recurrent unit to classify sentences of abstracts. The proposed neural network was tested on a sample of 20 thousand abstracts from the biomedical domain. Competitive results were achieved, with weight-averaged precision, recall and F1-score values around 91%, which are higher when compared to a state-of-the-art neural network.
Highlights
This study proposes a novel deep learning approach based on a convolutional layer and a bi-directional gated recurrent unit to classify sentences of abstracts
The proposed Word-BiGRU deep learning architecture is compared with two other approaches: a similar model that does not include the bi-directional Gated Recurrent Unit (GRU) layer (CNN model), and with the results provided in [6] (Char-BiLSTM)
Word-BiGRU shows competitive results when compared with Char-BiLSTM
Summary
There has been a rise in the number of scholarly publications [14]. Around 114 million of English scholarly documents were accessible on the Web in 2014 [9] Such volume makes it difficult to quickly select relevant scientific documents. The classification of scientific abstracts is a particular instance of the sequential classification task, considering there is a typical order in the classes (e.g., the ‘Objective’ label tends to appear after the ‘Background’). This classification transforms unstructured text into a more information manageable structure [6]. It is a valuable tool for general scientific database querying (e.g., using Web of Science, Scopus) It can assist in manual [11] or text mining [15] systematic literature review processes, as well as other bibliometric analyses.
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