Automatic Extractive Summarization for Japanese Academic Papers by LDA
Abstract
The demand for automatic summarization of newspaper headlines and article is increasing and various studies on automatic summarization are currently conducted.
However, there are only a few studies on summarization of Japanese documents compared to those for English documents.
In this study, we verify whether existing summarization methods can be effective for academic papers written in Japanese. First, we demonstrate the effectiveness of topic-based extrac tive summarization methods Latent Semantic Analysis (LSA). We then show that more effective topic-based extractive summarization can be achieved by using Latent Dirichlet Allocation ( LDA).
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