| 基于Uni LM模型的学术文摘观点自动生成研究 |
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| 引用本文： | 曾江峰,刘园园,程征,段尧清.基于Uni LM模型的学术文摘观点自动生成研究[J].图书情报工作,2023,67(2):131-139. |
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| 作者姓名： | 曾江峰 刘园园 程征 段尧清 |
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| 作者单位： | 华中师范大学信息管理学院 武汉 430079 |
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| 基金项目： | 本文系教育部人文社会科学青年项目“情境大数据驱动的社交媒体虚假信息识别模型与治理策略研究”（项目编号：21YJC870002）和中央高校基本科研业务费资助项目“信息交互行为与隐私保护研究”（项目编号：CCNU22QN017）研究成果之一。 |
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| 摘 要： | 目的 /意义]将海量学术文本观点提取工作由人工转向机器，提高效率的同时又能够保证观点提取的准确性、客观性。方法 /过程]使用UniLM统一语言预训练模型，训练过程中对模型进行精调，以人工标注数据集进行机器学习。将学术文摘作为长度为a的文本序列,经过机器学习，生成长度为b的句子序列(a≥b)，并且作为学术论文观点句输出。结果 /结论 ]研究结果表明：UniLM模型对于规范型文摘、半规范型文摘、非规范型文摘观点生成精准度分别为94.36%、77.27%、57.43%，规范型文摘生成效果最好。将机器学习模型应用于长文本观点生成，为学术论文观点生成提供一种新方法。不足之处在于本文模型依赖文摘的结构性，对非规范型文摘观点生成效果有所欠缺。
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| 关 键 词： | 学术文摘 观点自动生成 UniLM模型 机器学习 |
| 收稿时间： | 2022-07-25 |
| 修稿时间： | 2022-10-06 |
An Automatic Generation Study of Academic Abstract Viewpoints Based on the UniLM Model |
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| Zeng Jiangfeng,Liu Yuanyuan,Cheng Zheng,Duan Yaoqing.An Automatic Generation Study of Academic Abstract Viewpoints Based on the UniLM Model[J].Library and Information Service,2023,67(2):131-139. |
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| Authors: | Zeng Jiangfeng Liu Yuanyuan Cheng Zheng Duan Yaoqing |
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| Institution: | School of Information Management, Central China Normal University, Wuhan 430079 |
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| Abstract: | Purpose/Significance] The extraction of views from massive academic texts has shifted from manual to machine, which improves efficiency and ensures the accuracy and objectivity of point of view extraction.Method/Process] Pre-train models using UniLM unified language, fine-tuning the model during training, and machine learning with manually labeled datasets. Using the academic abstract as a sequence of text of length a, after machine learning, it was possible to generate a sentence sequence of length b (a ≥ b) and output as an academic paper point of view sentence.Result/Conclusion] The results show that the UniLM model has the best effect on the generation of normative abstracts with 94.36%, semi-canonical abstracts with 77.27%, and non-normative abstracts with 57.43%. Applying machine learning models to long text idea generation provides a new approach to academic paper idea generation. The disadvantage is that the model of this paper relies on the structure of the abstract, and the effect of generating non-normative abstract views is lacking. |
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| Keywords: | academic abstracts automatic generation of ideas UniLM models machine learning |
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