Content area
Purpose
>Reorganising unstructured academic abstracts according to a certain logical structure can help scholars not only extract valid information quickly but also facilitate the faceted search of academic literature. This study aims to build a high-performance model for identifying of the functional structures of unstructured abstracts in the social sciences.
Design/methodology/approach
>This study first investigated the structuring of abstracts in academic articles in the field of social sciences, using large-scale statistical analyses. Then, the functional structures of sentences in the abstract in a corpus of more than 3.5 million abstracts were identified from sentence classification and sequence tagging by using several models based on either machine learning or a deep learning approach, and the results were compared.
Findings
>The results demonstrate that the functional structures of sentences in abstracts in social science manuscripts include the background, purpose, methods, results and conclusions. The experimental results show that the bidirectional encoder representation from transformers exhibited the best performance, the overall F1 score of which was 86.23%.
Originality/value
>The data set of annotated social science abstract is generated and corresponding models are trained on the basis of the data set, both of which are available on Github (https://github.com/Academic-Abstract-Knowledge-Mining/SSCI_Abstract_Structures_Identification). Based on the optimised model, a Web application for the identification of the functional structures of abstracts and their faceted search in social sciences was constructed to enable rapid and convenient reading, organisation and fine-grained retrieval of academic abstracts.
Details
Machine learning;
Datasets;
Deep learning;
Computer science;
Applications programs;
Identification;
Classification;
Manuscripts;
Literature reviews;
Algorithms;
Coders;
Statistical analysis;
Social sciences;
Models;
Social research;
Bidirectionality;
Sentence structure;
Academic achievement;
Retrieval;
Software
1 School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China
2 School of Information Management, Nanjing Agricultural University, Nanjing, China
3 School of Information Management, Nanjing University, Nanjing, China and Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing University, Nanjing, China