Abstract
In our research, we applied Probabilistic Latent Semantic Analysis (PLSA) to single-document summarization. PLSA is based on Aspect model which can be used to analyze co-occurrence of terms and sentences. PLSA had been already proved that it performs better than Latent Semantic Analysis (LSA) in automatic indexing domain. In our research, we attempt to apply PLSA to solve automatic summarization problem. In literature, most of automatic summarizers were built on corpus-based structure. However, a corpus-based automatic summarizer requires a lot of documents and artificial summaries for training. Moreover, it will be hindered by the shortage of training documents on emerging topics. As so, we applied non-corpus based technique for automatic summarizer builder. A modified PLSA is proposed to build a summarizer. The performance of PLSA was compared with that of LSA and Relevance Measure (RM) summarizer. Using New Taiwan Magazine data, the results indicate that RM summarizer performed the best, PLSA summarizer ranked second, and LSA summarizer performed the worst.