Abstract
In this paper, we attempt to extract and generate the short summary for the news article with the length limit of 60 Chinese characters. Firstly, we preprocess the news article by segmenting sentences and words, and then extract four kinds of central words to form the keyword dictionary based on parsing tree. After that, the four kinds of features, i.e. the sentence weight, the sentence similarity, the sentence position and the length of sentence, will be employed to measure the significance of each sentence. Finally, we extract two sentences in the descending order of significance score and compress them to get the summary for each news article. This approach can analyze the grammatical elements from original sentences in order to generate compression rules and trim syntactic elements according to their parsing trees. The evaluation results show that our system is efficient in Chinese news summarization.
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Acknowledgments
The work presented in this paper is partially supported by the Major Projects of National Social Science Foundation of China under No. 11&ZD189, Natural Science Foundation of China under No. 61402341, Planning Foundation of Wuhan Science and Technology Bureau under No. 2016060101010047, and Open Foundation of Hubei Province Key Laboratory under No. 2016znss05A.
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Liu, M., Yu, Y., Qi, Q., Hu, H., Ren, H. (2018). Extractive Single Document Summarization via Multi-feature Combination and Sentence Compression. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_70
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DOI: https://doi.org/10.1007/978-3-319-73618-1_70
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