Abstract
Text summarization aims at getting the most important content in a condensed form from a given document while retains the semantic information of the text to a large extent. It is considered to be an effective way of tackling information overload. There exist lots of text summarization approaches which are based on Latent Semantic Analysis (LSA). However, none of the previous methods consider the term description of the topic. In this paper, we propose a comprehensive LSA-based text summarization algorithm that combines term description with sentence description for each topic. We also put forward a new way to create the term by sentence matrix. The effectiveness of our method is proved by experimental results. On the summarization performance, our approach obtains higher ROUGE scores than several well known methods.
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Wang, Y., Ma, J. (2013). A Comprehensive Method for Text Summarization Based on Latent Semantic Analysis. In: Zhou, G., Li, J., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2013. Communications in Computer and Information Science, vol 400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41644-6_38
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DOI: https://doi.org/10.1007/978-3-642-41644-6_38
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