Abstract
Multi-document summarization is a difficult natural language processing task. Many extractive summarization methods consist of two steps: extract important concepts of documents and select sentences based on those concepts. In this paper, we introduce a method to use the Latent Dirichlet Allocation (LDA) topic labels as concepts, instead of n-gram or using external resources. Sentences are selected based on these topic labels in order to form a summary. Two selection methods are proposed in the paper. Experiments on DUC2004 dataset has shown that Vector-based methods are better, i.e. map topic labels and sentences to a word vector and a letter trigram vector space to find those sentences which are syntactically and semantically related with the topic labels in order to form a summary. Experiments show that the produced summaries are informative, abstractive and better than the baseline method.
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Acknowledgement
We would like to thank the National Natural Science Foundation of China (Grant No. 61375053) for part of the financial support of this paper.
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Kou, W., Li, F., Ye, Z. (2017). Using Topic Labels for Text Summarization. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_46
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DOI: https://doi.org/10.1007/978-3-319-60045-1_46
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