Using n-best recognition output for extractive summarization and keyword extraction in meeting speech

Liu, Yang; Xie, Shasha; Liu, Fei

doi:10.1109/icassp.2010.5494972

Cited by 20 publications

(10 citation statements)

References 14 publications

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“…Information from the speech signal, such as prosody, energy, and F0 provides good cues to spoken document structure or rhetorical structure and can help a summarizer to identify important content [Maskey and Hirschberg 2003Hori and Furui 2003;Inoue et al 2004;Zhang et al 2007a;Fung et al 2008;Xie et al 2010a;Murray et al 2010;Zhu and Penn 2006;Liu et al 2010]. Maskey and Hirschberg [2003] extracted structural features from audio documents to help summarization.…”

Section: Speech Summarizationmentioning

confidence: 99%

Active learning with semi-automatic annotation for extractive speech summarization

Zhang

Fung

2012

ACM Trans. Speech Lang. Process.

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We propose using active learning for extractive speech summarization in order to reduce human effort in generating reference summaries. Active learning chooses a selective set of samples to be labeled. We propose a combination of informativeness and representativeness criteria for selection. We further propose a semiautomatic method to generate reference summaries for presentation speech by using Relaxed Dynamic Time Warping (RDTW) alignment between presentation speech and its accompanied slides. Our summarization results show that the amount of labeled data needed for a given summarization accuracy can be reduced by more than 23% compared to random sampling. ACM Reference Format:Zhang, J. J. and Fung, P. 2012. Active learning with semi-automatic annotation for extractive speech summarization.

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Section: Speech Summarizationmentioning

confidence: 99%

Active learning with semi-automatic annotation for extractive speech summarization

Zhang

Fung

2012

ACM Trans. Speech Lang. Process.

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“…In addition to feature selection, this study has also explored weighting. tf-idf weighting in the summary system has been used by many researchers [15], [24] and the obtained result is quite good. However, some researchers use only weighting tf.…”

Section: Weightingmentioning

confidence: 99%

“…The techniques explored are differentiated into feature selection [7]- [14], weighting [15]- [17] and MMR. This is due to their simplicity, effectiveness and they yield relevant and non-exaggerated outputs [15], [18]- [21]. Vishal Gupta [9] used cue method, title, and location sentences as query or keyword.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Performance in Medical Articles Summarization with Multi-Feature Selection

Bhaskoro¹,

Akbar²,

Supangkat³

2018

IJECE

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<span>The research aimed at providing an outcome summary of extraordinary events information for public health surveillance systems based on the extraction of online medical articles. The data set used is 7,346 pieces. Characteristics possessed by online medical articles include paragraphs that comprise more than one and the core location of the story or important sentences scattered at the beginning, middle and end of a paragraph. Therefore, this study conducted a summary by maintaining important phrases related to the information of extraordinary events scattered in every paragraph in the medical article online. The summary method used is maximal marginal relevance with an n-best value of 0.7. While the multi feature selection in question is the use of features to improve the performance of the summary system. The first feature selection is the use of title and statistic number of word and noun occurrence, and weighting tf-idf. In addition, other features are word level category in medical content patterns to identify important sentences of each paragraph in the online medical article. The important sentences defined in this study are classified into three categories: core sentence, explanatory sentence, and supporting sentence. The system test in this study was divided into two categories, such as extrinsic and intrinsic test. Extrinsic test is comparing the summary results of the decisions made by the experts with the output resulting from the system. While intrinsic test compared three n-Best weighting value method, feature selection combination, and combined feature selection combination with word level category in medical content. The extrinsic evaluation result was 72%. While intrinsic evaluation result of feature selection combination merger method with word category in medical content was 91,6% for precision, 92,6% for recall and f-measure was 92,2%.</span>

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“…While most work focused primarily on news content, recent effort has been increasingly directed towards new domains such as lectures [2,3] and multi-party interaction [4,5,6]. In this work, we perform extractive summarization on the output of automatic speech recognition (ASR) and corresponding manual transcripts [7] of multi-party "meeting" recordings.…”

Section: Introductionmentioning

confidence: 99%

Two-layer mutually reinforced random walk for improved multi-party meeting summarization

Chen

Metze

2012

2012 IEEE Spoken Language Technology Workshop (SLT)

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This paper proposes an improved approach of summarization for spoken multi-party interaction, in which a two-layer graph with utterance-to-utterance, speaker-to-speaker, and speakerto-utterance relations is constructed. Each utterance and each speaker are represented as a node in the utterance-layer and speaker-layer of the graph respectively, and the edge between two nodes is weighted by the similarity between the two utterances, the two speakers, or the utterance and the speaker. The relation between utterances is evaluated by lexical similarity via word overlap or topical similarity via probabilistic latent semantic analysis (PLSA). By within-and between-layer propagation in the graph, the scores from different layers can be mutually reinforced so that utterances can automatically share the scores with the utterances from the same speaker and similar utterances. For both ASR output and manual transcripts, experiments confirmed the efficacy of involving speaker information in the two-layer graph for summarization.

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Using n-best recognition output for extractive summarization and keyword extraction in meeting speech

Cited by 20 publications

References 14 publications

Active learning with semi-automatic annotation for extractive speech summarization

Active learning with semi-automatic annotation for extractive speech summarization

Enhancing Performance in Medical Articles Summarization with Multi-Feature Selection

Two-layer mutually reinforced random walk for improved multi-party meeting summarization

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