Work

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

Year: 2023

Type: conference-paper

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.

Source: International Computer Conference, Computer Society of Iran (CSICC)

DOI: 10.1109/csicc58665.2023.10105403

Authors: Majid Ramezani, Mohammad-Salar Shahryari, Amir-Reza Feizi-Derakhshi, Mohammad-Reza Feizi-Derakhshi

Institution: University of Tabriz

Language: English

Topic: Advanced Text Analysis Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4367146346

FWCI: 1.247

Cites: 45

Cited by: 11

Related to: 10