Abstract
This paper proposes an abstractive multi-document summarization method. Given a document set, the system first generates sentence clusters through an event clustering algorithm using distributed representation. Each cluster is regarded as a subtopic of this set. Then we use a novel multi-sentence compression method to generate K-shortest paths for each cluster. Finally, some preferable paths are selected from these candidates to construct the final summary based on several customized submodular functions, which are designed to measure the summary quality from different perspectives. Experimental results on DUC 2005 and DUC 2007 datasets demonstrate that our method achieves better performance compared with the state-of-the-art systems.
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Notes
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We use Sent2Vec, which code is available at https://github.com/klb3713/sentence2vec, to learn the vectors of sentences.
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Acknowledgments
We thank all reviewers for their detailed comments. This work is supported by the State Key Program of National Natural Science Foundation of China (Grant 61133012), the National Natural Science Foundation of China (Grant 61373108, 61373056), the National Philosophy Social Science Major Bidding Project of China (Grant 11&ZD189). The corresponding author of this paper is Donghong Ji.
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Sun, R., Wang, Z., Ren, Y., Ji, D. (2016). Query-Biased Multi-document Abstractive Summarization via Submodular Maximization Using Event Guidance. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9658. Springer, Cham. https://doi.org/10.1007/978-3-319-39937-9_24
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