Research on Intelligent Generation of Structured Review for Retrieval Result Set
[Purpose/Significance]In the academic document retrieval and reading,the current amount of academic information has far exceeded the user's information processing ability and caused information accumulation.In order to improve users'reading efficiency and knowledge absorption,this paper conducts comprehensive mining and revealing of the academic document retrieval result set.[Method/Process]On one hand,based on the reading experience and the document retrieval scenarios,it carried out a structured review expression design.On the other hand,starting from the improvement of technical methods and con-tent quality,it utilized deep learning based text automatic generation technology to construct an academic document dataset,trained and optimized a text abstract model,and used large language model technology to achieve structured review text generation.[Result/Conclusion]The optimized abstract model has an average increase of 2.07％in the recall rate and Fl value of each indicator after training.Structured review generation based on the big model can effectively extract and summarize the main points of the content in the actual evaluation,which verifies the feasibility of the technology roadmap and application practice,and provides a guide for the knowledge-based service level of academic literature,intelligent assisted reading and comprehensive mining and disclosure of semantic content.
literature searchstructured overviewlarge language modelautomatic text generation
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