  The methods of automatic speech summarization are classified into two groups:
supervised and unsupervised methods. Supervised methods are based on a set of
features, while unsupervised methods perform summarization based on a set of
rules. Latent Semantic Analysis (LSA) and Maximal Marginal Relevance (MMR) are
considered the most important and well-known unsupervised methods in automatic
speech summarization. This study set out to investigate the performance of two
aforementioned unsupervised methods in transcriptions of Persian broadcast news
summarization. The results show that in generic summarization, LSA outperforms
MMR, and in query-based summarization, MMR outperforms LSA in broadcast news
summarization.
