Abstract
In recent years graph-based ranking algorithms have attracted much attention in document summarization. This paper introduces our recent work on applying a topic model, namely LDA, in graph-based summarization. In the proposed approach, LDA is used to automatically identify a set of semantic topics from the documents to be summarized. The identified topics are then used to construct a bipartite graph to represent the documents. Topic-sentence reinforcement is implemented to calculate the salience scores of topics and sentences simultaneously. By incorporating the information embedded in the topics, the sentence ranking result can be improved. Experiments are conducted on the DUC 2004 data set to evaluate the effectiveness of the proposed approach.
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© 2012 Springer-Verlag Berlin Heidelberg
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Gao, D., Li, W., Ouyang, Y., Zhang, R. (2012). LDA-Based Topic Formation and Topic-Sentence Reinforcement for Graph-Based Multi-document Summarization. In: Hou, Y., Nie, JY., Sun, L., Wang, B., Zhang, P. (eds) Information Retrieval Technology. AIRS 2012. Lecture Notes in Computer Science, vol 7675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35341-3_33
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DOI: https://doi.org/10.1007/978-3-642-35341-3_33
Publisher Name: Springer, Berlin, Heidelberg
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