Multi-document text summarization using topic model and fuzzy logic

Authors:
Sanghoon Lee;Saeid Belkasim;Yanqing Zhang
Affiliations:
Computer Science, Georgia State University, Atlanta, Georgia;Computer Science, Georgia State University, Atlanta, Georgia;Computer Science, Georgia State University, Atlanta, Georgia
Venue:
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2013

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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.