Most previous studies on speech summarization focus on the extractive approaches. Yet
directly concatenating the extracted speech utterances may not form a good summary due to …

Speech summarisation techniques take human speech as input and then output an
abridged version as text or speech. Speech summarisation has applications in many …

Extractive speech summarization attempts to select a representative set of sentences from a
spoken document so as to succinctly describe the main theme of the original document. In …

This paper proposes a new automatic speech summarization method having two stages:
important sentence extraction and sentence compaction. Relatively important sentences are …

Extractive text or speech summarization endeavors to select representative sentences from
a source document and assemble them into a concise summary, so as to help people to …

In this paper, we propose a novel architecture for direct extractive speech-to-speech
summarization, ESSumm, which is an unsupervised model without dependence on …

This paper proposes a statistical approach to automatic speech summarization. In our
method, a set of words maximizing a summarization score indicating the appropriateness of …

Word embedding methods revolve around learning continuous distributed vector
representations of words with neural networks, which can capture semantic and/or syntactic …

End-to-end speech summarization (E2E SSum) is a technique to directly generate summary
sentences from speech. Compared with the cascade approach, which combines automatic …

This dissertation proposes a new automatic speech summarization method through word
extraction. In this method, a set of words maximizing a summarization score indicating an …