Abstract
In this paper, we put forward UIDS, a new high-performing extensible framework for extractive MultiLingual Document Summarization. Our approach looks on a document in a multilingual corpus as an item sequence set, in which each sentence is an item sequence and each item is the minimal semantic unit. Then we formalize the extractive summary as summary diversity sampling problem that considers topic diversity and redundancy at the same time. The topic diversity is reflected using hierarchical topic models, the redundancy is reflected using similarity and the summary diversity is enhanced using Determinantal Point Processes. We then illustrate how this method encompasses a framework that is amenable to compute summaries for MultiLingual Single- and Multi-documents. Experiments on the MultiLing summarization task datasets demonstrate the effectiveness of our approach.
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Acknowledgements
This work was supported by the National Social Science Foundation of China under Grant 16ZDA055; National Natural Science Foundation of China under Grant 91546121, 71231002 and 61202247; EU FP7 IRSES MobileCloud Project 612212; the 111 Project of China under Grant B08004; Engineering Research Center of Information Networks, Ministry of Education; the project of Beijing Institute of Science and Technology Information; the project of CapInfo Company Limited.
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Li, L., Zhang, Y., Chi, J., Huang, Z. (2017). UIDS: A Multilingual Document Summarization Framework Based on Summary Diversity and Hierarchical Topics. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_29
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