Abstract
Extractive speech summarization can be thought of as a decision-making process where the summarizer attempts to select a subset of informative sentences from the original document. Meanwhile, a sentence being selected as part of a summary is typically determined by three primary factors: significance, relevance and redundancy. To meet these specifications, we recently presented a novel probabilistic framework stemming from the Bayes decision theory for extractive speech summarization. It not only inherits the merits of several existing summarization techniques but also provides a flexible mechanism to render the redundancy and coherence relationships among sentences and between sentences and the whole document, respectively. In this paper, we propose several new approaches to the ranking strategy and modeling paradigm involved in such a framework. All experiments reported were carried out on a broadcast news speech summarization task; very promising results were demonstrated.
| Original language | English |
|---|---|
| Pages | 1684-1687 |
| Number of pages | 4 |
| Publication status | Published - 2010 Dec 1 |
| Event | 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 - Makuhari, Chiba, Japan Duration: 2010 Sep 26 → 2010 Sep 30 |
Other
| Other | 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 |
|---|---|
| Country | Japan |
| City | Makuhari, Chiba |
| Period | 10/9/26 → 10/9/30 |
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Keywords
- Bayes decision theory
- Decision making
- Extractive speech summarization
ASJC Scopus subject areas
- Language and Linguistics
- Speech and Hearing
Cite this
Extractive speech summarization - From the view of decision theory. / Lin, Shih Hsiang; Yeh, Yao Ming; Chen, Berlin.
2010. 1684-1687 Paper presented at 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010, Makuhari, Chiba, Japan.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Extractive speech summarization - From the view of decision theory
AU - Lin, Shih Hsiang
AU - Yeh, Yao Ming
AU - Chen, Berlin
PY - 2010/12/1
Y1 - 2010/12/1
N2 - Extractive speech summarization can be thought of as a decision-making process where the summarizer attempts to select a subset of informative sentences from the original document. Meanwhile, a sentence being selected as part of a summary is typically determined by three primary factors: significance, relevance and redundancy. To meet these specifications, we recently presented a novel probabilistic framework stemming from the Bayes decision theory for extractive speech summarization. It not only inherits the merits of several existing summarization techniques but also provides a flexible mechanism to render the redundancy and coherence relationships among sentences and between sentences and the whole document, respectively. In this paper, we propose several new approaches to the ranking strategy and modeling paradigm involved in such a framework. All experiments reported were carried out on a broadcast news speech summarization task; very promising results were demonstrated.
AB - Extractive speech summarization can be thought of as a decision-making process where the summarizer attempts to select a subset of informative sentences from the original document. Meanwhile, a sentence being selected as part of a summary is typically determined by three primary factors: significance, relevance and redundancy. To meet these specifications, we recently presented a novel probabilistic framework stemming from the Bayes decision theory for extractive speech summarization. It not only inherits the merits of several existing summarization techniques but also provides a flexible mechanism to render the redundancy and coherence relationships among sentences and between sentences and the whole document, respectively. In this paper, we propose several new approaches to the ranking strategy and modeling paradigm involved in such a framework. All experiments reported were carried out on a broadcast news speech summarization task; very promising results were demonstrated.
KW - Bayes decision theory
KW - Decision making
KW - Extractive speech summarization
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