Abstract
The past several years have witnessed the rapid development of social media services, and the UGCs (User Generated Contents) have been increased dramatically, such as tweets in Twitter and posts in Sina Weibo. In this paper, we describe our system at NLPCC2015 on the Weibo-oriented Chinese news summarization task. Our model is established based on multi-feature combination to automatically generate summary for the given news article. In our system, we mainly utilize four kinds of features to compute the significance score of a sentence, including term frequency, sentence position, sentence length and the similarity between sentence and news article title, and then the summary sentences are chosen according to the significance score of each sentence from the news article. The evaluation results on Weibo news document sets show that our system is efficient in Weibo-oriented Chinese news summarization and outperforms all the other systems.
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Liu, M., Wang, L., Nie, L. (2015). Weibo-Oriented Chinese News Summarization via Multi-feature Combination. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_55
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DOI: https://doi.org/10.1007/978-3-319-25207-0_55
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