| dc.description.abstract | With the rapid development of news media, and the news is a series of document stream. In the past, the production methods of news summary were based on NGD method, it found the keywords which were highly correlated to the title. However, because that method is through the Solr full text search system, it would take lots of time. In the other way, there are still a lot of improvements in quality for the unsupervised graph-based method, since the result of the sentence network is not as good as expected. Nevertheless, when used the techniques for the English summaries in Chinese summaries directly, the quality and efficiency are still not as good as expected.
In this study, I enhance the Chinese word segmentation with increasing the Chinese part of speech recognition. In addition, I take into account the positions of the sentence through adopting the TextRank-based keyword extraction and link-analysis method. Eventually, not only it improves the quality of the single document, but also the speed is well improved.
At last, based on the single document summary method, I use the sentence grouping in the waterfall architecture to produce the dynamic multi-document summary. It can produce the summary with the evolution of time, and also filter the redundant message in the documents. | en_US |