Constructing Automatic Structured Synthesis Tool for Sci-Tech Literature Based on Move Recognition
[Objective]This paper utilizes Al technology to construct an automatic structured synthesis tool,which organizes the sci-tech research frameworks structurally and reveals their main points.[Methods]The new tool was developed based on move recognition.First,we identified the research questions,methodology,and progress keywords to extract the most important knowledge points from each literature.Then,we employed hierarchical clustering and cluster label generation methods to synthesize the knowledge.Third,we designed a tree structure for the synthesis outputs.[Results]The proposed tool could automatically synthesize the literature contents and reveal their framework with a"research question,methodology,and progress"tree structure.[Limitations]Insufficient clustering accuracy and difficulty determining cluster numbers reduce our model's synthesis performance.[Conclusions]The synthesis tool based on move recognition could automatically retrieve structured literature contents.
Scientific and Technological LiteratureMove RecognitionAutomatic Structured SynthesisPhrase ExtractionHierarchical ClusteringLabel Generation
NETL
NSTL
万方数据
