#3684. Symbolic and Statistical Learning Approaches to Speech Summarization: A Scoping Review

October 2026publication date
Proposal available till 03-06-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Language and Linguistics;
Linguistics and Language;
Sociology and Political Science;
Speech and Hearing;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №3684.1 Contract №3684.2 Contract №3684.3 Contract №3684.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

Abstract:
Speech summarization techniques take human speech as input and then output an abridged version as text or speech. Speech summarization has applications in many domains from information technology to health care, for example improving speech archives or reducing clinical documentation burden. Most studies employ one of four speech summarization architectures: (1) Sentence extraction and compaction; (2) Feature extraction and classification or rank-based sentence selection; (3) Sentence compression and compression summarization; and (4) Language modelling. We also discuss the strengths and weaknesses of these different methods and speech features. As supervised methods require manually annotated training data which can be costly, there was more interest in unsupervised methods.
Keywords:
Abstractive summarization; Automatic speech recognition; Extractive summarization; Speech summarization; Spontaneous speech

Contacts :
0