Unsupervised Broadcast News Summarization; a Comparative Study on Maximal Marginal Relevance (MMR) and Latent Semantic Analysis (LSA)

Abstract

The automatic speech summarization methods traditionally are classified into two groups: supervised and unsupervised methods. Supervised methods rely on a set of features, while unsupervised methods perform summarization through a set of rules. Among unsupervised automatic speech summarization methods, Latent Semantic Analysis (LSA) and Maximal Marginal Relevance (MMR) are so famous. This study set out to peruse the overall efficacy of two aforementioned unsupervised methods in summarization of Persian broadcast news transcriptions. The results justify the superiority of LSA to MMR during generic summarization. This is while MMR achieves better results in query-based summarization.