Structured Abstract Generation for Scientific and Technological Papers by Integrating Moves and Text Features
Xi Haixu1,2, He Sheng1, Huang Chunguo1
1.School of Computer Engineering, Jiangsu University of Technology, Changzhou 213001 2.Department of Information Management, School of Economics & Management, Nanjing University of Science & Technology, Nanjing 210094
Abstract In the era of mobile Internet, mobile and fragmented reading have become the main means of public reading. One of the important ways to solve the problem of information overload is to provide key summary content for improving reading efficiency. The task of abstracting scientific and technological papers is more challenging than that of abstracting ordinary texts, such as news, because of their length, varying content, and domain knowledge. This paper proposes a structured summarization method for scientific papers. First, scientific papers are divided into different moves, and then, the texts of different moves are abstracted separately. The multiple features of the text are integrated into the iterative calculation process of the TextRank algorithm according to weight, and the MMR algorithm is introduced to redundantly process the pre-selected abstract set. Finally, the text is semantically analyzed using dependency syntax analysis, and the summary is further streamlined and combined into a structured summary. Experimental results show that this method is different from the benchmark model in terms of the relevance, diversity, and readability of different moves. Combined with manual evaluation, this method can significantly improve the diversity of the summary while simultaneously improving the relevance and readability of the summary to a certain extent.
Xi Haixu,He Sheng,Huang Chunguo. Structured Abstract Generation for Scientific and Technological Papers by Integrating Moves and Text Features[J]. 情报学报, 2023, 42(10): 1176-1186.
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