Extractive broadcast news summarization leveraging recurrent neural network language modeling techniques

Kuan Yu Chen, Shih Hung Liu, Berlin Chen*, Hsin Min Wang, Ea Ee Jan, Wen Lian Hsu, Hsin Hsi Chen


研究成果: 雜誌貢獻期刊論文同行評審

36 引文 斯高帕斯(Scopus)


Extractive text or speech summarization manages to select a set of salient sentences from an original document and concatenate them to form a summary, enabling users to better browse through and understand the content of the document. A recent stream of research on extractive summarization 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 sentence in the document to be summarized. In view of this, our work in this paper explores a novel use of recurrent neural network language modeling (RNNLM) framework for extractive broadcast news summarization. On top of such a framework, 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 broadcast news documents, getting around the need for the strict bag-of-words assumption. Furthermore, different model complexities and combinations are extensively analyzed and compared. Experimental results demonstrate the performance merits of our summarization methods when compared to several well-studied state-of-the-art unsupervised methods.

頁(從 - 到)1322-1334
期刊IEEE Transactions on Audio, Speech and Language Processing
出版狀態已發佈 - 2015 8月 1

ASJC Scopus subject areas

  • 聲學與超音波
  • 電氣與電子工程


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