Abstract
The automation of the process of summarizing documents plays a major rule in many applications. Automatic Text Summarization has been focused on retaining the essential information without affecting the document quality. This paper proposes a new multi-document summarization method that combines topic model and fuzzy logic model. The proposed method extracts some relevant topic words from source documents. The extracted words are used as elements of fuzzy sets. Meanwhile, each sentence on the source document is used to generate a fuzzy relevance rule that measures the importance of each sentence. A fuzzy inference system is used to generate the final summarization. Our summarization results are evaluated against some well-known summary systems and performed well in divergences and similarities.
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Lee, S., Belkasim, S., Zhang, Y. (2013). Multi-document Text Summarization Using Topic Model and Fuzzy Logic. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science(), vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_12
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DOI: https://doi.org/10.1007/978-3-642-39712-7_12
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