Survey generation aims to generate a summary from a scientific topic based on related papers.The structure of papers deeply influences the generative process of survey,especially the relationships between sentence and...Survey generation aims to generate a summary from a scientific topic based on related papers.The structure of papers deeply influences the generative process of survey,especially the relationships between sentence and sentence,paragraph and paragraph.In principle,the structure of paper can influence the quality of the summary.Therefore,we employ the structure of paper to leverage contextual information among sentences in paragraphs to generate a survey for documents.In particular,we present a neural document structure model for survey generation.We take paragraphs as units,and model sentences in paragraphs,we then employ a hierarchical model to learn structure among sentences,which can be used to select important and informative sentences to generate survey.We evaluate our model on scientific document data set.The experimental results show that our model is effective,and the generated survey is informative and readable.展开更多
基金This work was supported by the Fundamental Research Funds for the Central Universities(2018B678X14 and 2016B44414)Postgraduate Research Practice Innovation Program of Jiangsu Province of China(KYCX18_0553 and KYLX16_0722)+1 种基金the National Natural Science Foundation of China(Grant Nos.61806137 and 61976146)Project of Natural Science Research of the Universities of Jiangsu Province(18KJB520043).
文摘Survey generation aims to generate a summary from a scientific topic based on related papers.The structure of papers deeply influences the generative process of survey,especially the relationships between sentence and sentence,paragraph and paragraph.In principle,the structure of paper can influence the quality of the summary.Therefore,we employ the structure of paper to leverage contextual information among sentences in paragraphs to generate a survey for documents.In particular,we present a neural document structure model for survey generation.We take paragraphs as units,and model sentences in paragraphs,we then employ a hierarchical model to learn structure among sentences,which can be used to select important and informative sentences to generate survey.We evaluate our model on scientific document data set.The experimental results show that our model is effective,and the generated survey is informative and readable.
