Identifying Structural Elements of Scholarly Abstracts with ERNIE-DPCNN
[Objective]This paper proposes an effective model to extract key elements from unstructured abstracts of academic literature automatically.[Methods]First,we used the ERNIE model to represent the abstracts.Then,we utilized the DPCNN to extract semantic features.Finally,we built the identification model.[Results]We evaluated the proposed model using a library and information science dataset.The precision,recall,and Fl-score values were all above 0.95,which outperformed benchmark models.[Limitations]Since the corpus used in this study is from a specific domain,more research is needed to assess the model's performance in other fields.[Conclusions]The proposed model can represent the abstract more comprehensively,improving the structural elements'identification performance from unstructured abstracts.
Structural Element Identification of AbstractsText RepresentationERNIEDPCNN
万方数据
维普
