A recurrent neural network language modeling framework for extractive speech summarization

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

研究成果: 雜誌貢獻Conference article同行評審

5 引文 斯高帕斯(Scopus)

摘要

Extractive speech summarization, with the purpose of automatically selecting a set of representative sentences from a spoken document so as to concisely express the most important theme of the document, has been an active area of research and development. A recent school of thought is to employ the language modeling (LM) approach for important sentence selection, which has proven to be effective for performing speech summarization in an unsupervised fashion. However, one of the major challenges facing the LM approach is how to formulate the sentence models and accurately estimate their parameters for each spoken document to be summarized. This paper presents a continuation of this general line of research and its contribution is two-fold. First, we propose a novel and effective recurrent neural network language modeling (RNNLM) framework for speech summarization, on top of which the deduced sentence models are able to render not only word usage cues but also long-span structural information of word co-occurrence relationships within spoken documents, getting around the need for the strict bag-of-words assumption. Second, the utilities of the method originated from our proposed framework and several widely-used unsupervised methods are analyzed and compared extensively. A series of experiments conducted on a broadcast news summarization task seem to demonstrate the performance merits of our summarization method when compared to several state-of-the-art existing unsupervised methods.

原文英語
文章編號6890220
期刊Proceedings - IEEE International Conference on Multimedia and Expo
2014-September
發行號Septmber
DOIs
出版狀態已發佈 - 2014 九月 3
事件2014 IEEE International Conference on Multimedia and Expo, ICME 2014 - Chengdu, 中国
持續時間: 2014 七月 142014 七月 18

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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