Scientific Literature Summarization Using Document Structure and Hierarchical Attention Model

Abstract

Scientific literature summarization aims to summarize related papers into a survey. It can help researchers, especially newcomers, to quickly know the current situation of their professional area from massive literature. Since the structure of documents is very important for scientific literature summarization, and it is obviously observed that there is a relationship and semantic information hidden in document structures of scientific literature, therefore, we employ a hierarchical attention model to learn document structure from all the papers for summarization. In particular, we utilize attention mechanism to capture relations among document structure and learn semantic information on document discourse levels, and judge the importance of each sentence according to its surroundings by the information obtained. Moreover, we automatically construct a scientific literature data set consisting of surveys and their references. We evaluate our proposed model on this dataset with ROUGE metrics, experiments prove that our approach is effective, and our model outperforms several baselines.