Speech summarization without lexical features for Mandarin broadcast news

Zhang, Jian; Fung, Pascale

doi:10.3115/1614108.1614162

Cited by 34 publications

(26 citation statements)

References 11 publications

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“…Probably the most striking finding that will need further verification and analysis in future work are results indicating that very good summarization performance can be achieved on the basis of acoustic features alone, with no recourse to transcripts or textual features [124,158,231].…”

Section: Summarization Of Speechmentioning

confidence: 99%

Automatic Summarization

Nenkova

2011

FNT in Information Retrieval

343

192

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Section: Summarization Of Speechmentioning

confidence: 99%

Automatic Summarization

Nenkova

2011

FNT in Information Retrieval

343

192

View full text Add to dashboard Cite

“…Among them, the vector space model (VSM) [9], the latent semantic analysis (LSA) method [9], the Markov random walk (MRW) method [10], the maximum marginal relevance (MMR) method [11], the sentence significant score method [12], the LexRank [13], the submodularity-based method [14], and the integer linear programming (ILP) method [15] are the most popular approaches for spoken document summarization. Apart from that, a number of classification-based methods using various kinds of representative features also have been investigated, such as the Gaussian mixture models (GMM) [9], the Bayesian classifier (BC) [16], the support vector machine (SVM) [17] and the conditional random fields (CRFs) [18], to name just a few. In these methods, important sentence selection is usually formulated as a binary classification problem.…”

Section: Introductionmentioning

confidence: 99%

A recurrent neural network language modeling framework for extractive speech summarization

Chen

Liu

Chen

et al. 2014

2014 IEEE International Conference on Multimedia and Expo (ICME)

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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.

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“…Besides traditional unsupervised summarization methods [3][4][5][6][7][8][9], such as those based on document structural, linguistic or prosodic information, proximity or significance measures and relevance scores to identify salient sentences, machinelearning approaches with supervised training have drawn much attention and been applied with good success in a wide arrange of summarization tasks [3][4][5][6][7][8][9]. Specifically, the problem of speech summarization can be formulated as follows: Construct a ranking model (summarizer) that assigns a decision score (or a posterior probability) of being included in the summary to each sentence of a spoken document to be summarized.…”

Section: Introductionmentioning

confidence: 99%

A margin-based discriminative modeling approach for extractive speech summarization

Liu

Chen

et al. 2014

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific

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Abstract-The task of extractive speech summarization is to select a set of salient sentences from an original spoken document and concatenate them to form a summary, facilitating users to better browse through and understand the content of the document. In this paper we present an empirical study of leveraging various supervised discriminative methods for effectively ranking important sentences of a spoken document to be summarized. In addition, we propose a novel margin-based discriminative training (MBDT) algorithm that aims to penalize non-summary sentences in an inverse proportion to their summarization evaluation scores, leading to better discrimination from the desired summary sentences. By doing so, the summarization model can be trained with an objective function that is closely coupled with the ultimate evaluation metric of extractive speech summarization. Furthermore, sentences of spoken documents are embodied by a wide range of prosodic, lexical and relevance features, whose utilities are extensively compared and analyzed. Experiments conducted on a Mandarin broadcast news summarization task demonstrate the performance merits of our summarization method when compared to several well-studied state-of-the-art supervised and unsupervised methods.

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Speech summarization without lexical features for Mandarin broadcast news

Cited by 34 publications

References 11 publications

Automatic Summarization

Automatic Summarization

A recurrent neural network language modeling framework for extractive speech summarization

A margin-based discriminative modeling approach for extractive speech summarization

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