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| Methodological and Automatic Sentence Extraction from Academic Article s Full-text |
| Zhang Yingyi, Zhang Chengzhi |
| Department of Information Management, School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094 |
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Abstract Research methods are essential in the scientific literature. These include methods, tools, or techniques for solving problems in the field. Theresearch method s description is usually presented through sentences. Summarizing these scattered sentences in the scientific literature can help researchers to quickly explore appropriate research methods. According to the method s purpose in the research paper, the research method sentence is further divided into method used and method cited sentences. The method used sentence refers to the sentence that describes the research method used in the paper and the method cited sentence refers to that cited by the paper. In this study, a variety of neural network-based sentence classification models are used for extracting the method sentences from the scientific literature s full-text. At the word vector representation layer, the study uses two-word vector models: BERT and word2vec. In the feature selection layer, three different networks are utilized: convolutional neural network (CNN), bidirectional LSTM (BiLSTM), and attention mechanism network. In addition, the study uses two model training methods a single-level structure and a two-level structure. The experimental results show that the BERT-based BiLSTM model with single-level structure achieves the best performance. This paper analyzes the distribution of research method sentences extracted from the Journal of The China Society for Scientific and Technical Information. The analysis indicates that this journal paid more attention to the theoretical developments of information science; in addition, the journal also focused on constructing theoretical systems for this discipline.
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Received: 13 October 2019
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