Leveraging manifold learning for extractive broadcast news summarization

Shih Hung Liu, Kuan Yu Chen, Berlin Chen, Hsin Min Wang, Wen Lian Hsu

研究成果: 書貢獻/報告類型會議貢獻

1 引文 斯高帕斯(Scopus)

摘要

Extractive speech summarization is intended to produce a condensed version of the original spoken document by selecting a few salient sentences from the document and concatenate them together to form a summary. In this paper, we study a novel use of manifold learning techniques for extractive speech summarization. Manifold learning has experienced a surge of research interest in various domains concerned with dimensionality reduction and data representation recently, but has so far been largely under-explored in extractive text or speech summarization. Our contributions in this paper are at least twofold. First, we explore the use of several manifold learning algorithms to capture the latent semantic information of sentences for enhanced extractive speech summarization, including isometric feature mapping (ISOMAP), locally linear embedding (LLE) and Laplacian eigenmap. Second, the merits of our proposed summarization methods and several widely-used methods are extensively analyzed and compared. The empirical results demonstrate the effectiveness of our unsupervised summarization methods, in relation to several state-of-the-art methods. In particular, a synergy of the manifold learning based methods and state-of-the-art methods, such as the integer linear programming (ILP) method, contributes to further gains in summarization performance.

原文英語
主出版物標題2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面5805-5809
頁數5
ISBN(電子)9781509041176
DOIs
出版狀態已發佈 - 2017 六月 16
事件2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, 美国
持續時間: 2017 三月 52017 三月 9

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(列印)1520-6149

會議

會議2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
國家美国
城市New Orleans
期間17/3/517/3/9

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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