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

arXiv: https://arxiv.org/abs/2301.02284 · HTML: https://mlsanity.com/paper/2301.02284/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 48 references that are in the map.
- First posted on arXiv 5 Jan 2023.

## Builds on (most cited first, 1 of 1)

- [Video Summarization via Actionness Ranking](https://mlsanity.com/paper/1903.00110/) (2019)

## Abstract (arXiv)

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.