Scientific Paper Summarization Using Word-Section Association

With the development of science and technology,people need to access a large number of scientific and technological information quickly,and scientific paper is one of the main ways to carry scientific and technological information.As an important part of scientific paper,abstract is an effective tool for readers to retrieve literature.Therefore,the quality of abstract affects the retrieval rate of paper directly.However,due to the lack of writing experience,the quality of abstracts written by many authors is not high.Automatic generation of summary for scientific paper can help the author grasp the important content of paper more effectively,so as to write high-quality abstract.At the same time,the automatically generated abstract can also control the number of words in the abstract,which can bring more content to readers and help them understand the paper better.Generating automa-tic summarization for scientific paper can help author write abstract faster,which is one of the research contents in automatic summarization.Compared with common news document,scientific paper has the characteristics of strong structure and clear logical relationship.As far as the mainstream abstractive summarization such as encoder-decoder model is concerned,it mainly consi-ders the serialized information in the document,and rarely explores the text structure information in the document.For this reason,according to the characteristics in scientific papers,this paper proposes an automatic summarization model based on the hie-rarchical structure of "word-section-document",which uses the association between word and section to enhance the level of text structure and the interaction between levels,so as to screen out the key information in scientific paper.In addition,a context gate unit is extended to update the optimized context vector,thus capturing context information more comprehensively.The experimental results show that the proposed model can effectively improve the performance of the generated summarization in the ROUGE evaluation method.