Enhanced language modeling for extractive speech summarization with sentence relatedness information

Shih Hung Liu*, Kuan Yu Chen, Yu Lun Hsieh, Berlin Chen, Hsin Min Wang, Hsu Chun Yen, Wen Lian Hsu

*此作品的通信作者

研究成果: 雜誌貢獻會議論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

Extractive summarization is intended to automatically select a set of representative sentences from a text or spoken document that can concisely express the most important topics of the document. Language modeling (LM) has been proven to be a promising framework for performing extractive summarization in an unsupervised manner. However, there remain two fundamental challenges facing existing LM-based methods. One is how to construct sentence models involved in the LM framework more accurately without resorting to external information sources. The other is how to additionally take into account the sentence-level structural relationships embedded in a document for important sentence selection. To address these two challenges, in this paper we explore a novel approach that generates overlapped clusters to extract sentence relatedness information from the document to be summarized, which can be used not only to enhance the estimation of various sentence models but also to allow for the sentencelevel structural relationships for better summarization performance. Further, the utilities of our proposed methods and several state-of-the-art unsupervised methods are analyzed and compared extensively. A series of experiments conducted on a Mandarin broadcast news summarization task demonstrate the effectiveness and viability of our method.

原文英語
頁(從 - 到)1865-1869
頁數5
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
出版狀態已發佈 - 2014 一月 1
事件15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014 - Singapore, 新加坡
持續時間: 2014 九月 142014 九月 18

ASJC Scopus subject areas

  • 語言與語言學
  • 人機介面
  • 訊號處理
  • 軟體
  • 建模與模擬

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