Abstract
Automatic summarization is an important research branch of natural language processing. The automatic summarization should provide information to users from different point of views for better understanding. Aiming at the characteristics of the news, an automatic summarization system is constructed from two aspects: keywords and key sentences. Then, the location factor is added to optimize the keywords extraction algorithm. Meanwhile, the key sentences extraction algorithm is improved through introducing keywords factors. On this basis, in allusion to the existing problems of RSS, this paper builds a user-interest model. Finally, after the verification in terms of the feasibility and the effectiveness, the result shows it is effective to improve the accuracy and the user experience of the RSS feeds.
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Wang, J., Ma, J., Li, Y. (2013). Design and Implementation of News-Oriented Automatic Summarization System Based on Chinese RSS. In: Zhou, G., Li, J., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2013. Communications in Computer and Information Science, vol 400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41644-6_36
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DOI: https://doi.org/10.1007/978-3-642-41644-6_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41643-9
Online ISBN: 978-3-642-41644-6
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