TY - GEN
T1 - Unsupervised Broadcast News Summarization; a Comparative Study on Maximal Marginal Relevance (MMR) and Latent Semantic Analysis (LSA)
AU - Ramezani, Majid
AU - Shahryari, Mohammad Salar
AU - Feizi-Derakhshi, Amir Reza
AU - Feizi-Derakhshi, Mohammad Reza
N1 - Publisher Copyright:
© 2023 IEEE.
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Latent Semantic Analysis (LSA)
KW - Maximal Marginal Relevance (MMR)
KW - broadcast news summarization
KW - unsupervised summarization
UR - http://www.scopus.com/inward/record.url?scp=85158130809&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85158130809&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/437eaad6-8884-31ed-92d8-21e1ebd8efb6/
U2 - 10.1109/CSICC58665.2023.10105403
DO - 10.1109/CSICC58665.2023.10105403
M3 - Conference contribution
AN - SCOPUS:85158130809
SN - 9798350338195
T3 - 2023 28th International Computer Conference, Computer Society of Iran (CSICC)
SP - 1
EP - 7
BT - 2023 28th International Computer Conference, Computer Society of Iran, CSICC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th International Computer Conference, Computer Society of Iran, CSICC 2023
Y2 - 25 January 2023 through 26 January 2023
ER -