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Dealing with the explosive growth of web sources on the Internet requires the use of efficient systems. Automatic text summarization is capable of addressing this issue. Recent years have seen remarkable success in the use of graph theory on text extractive summarization. However, the understanding of why and how they perform so well is still not clear. In this paper, we intend to seek a better understanding of graph models, which can benefit from graph extractive summarization. Additionally, analysis has been performed qualitatively with the graph models in the design of recent graph extractive summarization. Based on the knowledge acquired from the survey, our work could provide more clues for future research on extractive summarization. [ABSTRACT FROM AUTHOR]

With the rapid development of clinical and laboratory medicine, the field of bioinformatics boasts of extensive clinical records and research literature. Retrieving effective information from this huge data has become a challenging task. Hence, Intelligent text summarization, which enables users to find and understand relevant source texts more quickly and effortlessly, becomes a very significant and valuable field of research. In this study, we propose an improved TextRank algorithm with weight calculation based on sentence graph to solve this problem. For the experimental dataset obtained from Pubmed, we represent terms as vectors by using Skip-gram model. We design three methods which utilize word embeddings to calculate weights between sentences. Then we build an undirected graph with sentences as nodes. At last, we use the improved TextRank algorithm to calculate the importance of sentences and further generated summarizations base on its ranking. The experimental results and analysis on the datasets demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]