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

TL;DR

This paper compares the performance of two unsupervised speech summarization methods, LSA and MMR, on Persian broadcast news, revealing their relative strengths in generic and query-based summarization.

Contribution

It provides a comparative analysis of LSA and MMR in Persian broadcast news summarization, highlighting their effectiveness in different summarization scenarios.

Findings

LSA outperforms MMR in generic summarization

MMR outperforms LSA in query-based summarization

Unsupervised methods are effective for Persian broadcast news

Abstract

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.