Minimum cut model for spoken lecture segmentation

Malioutov, Igor; Barzilay, Regina

doi:10.3115/1220175.1220179

Cited by 133 publications

(153 citation statements)

References 32 publications

(23 reference statements)

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“…The method for segmentation that we have used is the minimum cut model from Malioutov and Barzilay ( [11]). This algorithm is based on sentence similarity.…”

Section: Probably the Most Well-known Methods Implementing This Ideas mentioning

confidence: 99%

Comparing segmentation strategies for efficient video passage retrieval

Wartena

2012

2012 10th International Workshop on Content-Based Multimedia Indexing (CBMI)

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We compare the effect of different text segmentation strategies on speech based passage retrieval of video. Passage retrieval has mainly been studied to improve document retrieval and to enable question answering. In these domains best results were obtained using passages defined by the paragraph structure of the source documents or by using arbitrary overlapping passages. For the retrieval of relevant passages in a video, using speech transcripts, no author defined segmentation is available. We compare retrieval results from 4 different types of segments based on the speech channel of the video: fixed length segments, a sliding window, semantically coherent segments and prosodic segments. We evaluated the methods on the corpus of the MediaEval 2011 Rich Speech Retrieval task. Our main conclusion is that the retrieval results highly depend on the right choice for the segment length. However, results using the segmentation into semantically coherent parts depend much less on the segment length. Especially, the quality of fixed length and sliding window segmentation drops fast when the segment length increases, while quality of the semantically coherent segments is much more stable. Thus, if coherent segments are defined, longer segments can be used and consequently less segments have to be considered at retrieval time.

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“…The method for segmentation that we have used is the minimum cut model from Malioutov and Barzilay ( [11]). This algorithm is based on sentence similarity.…”

Section: Probably the Most Well-known Methods Implementing This Ideas mentioning

confidence: 99%

Comparing segmentation strategies for efficient video passage retrieval

Wartena

2012

2012 10th International Workshop on Content-Based Multimedia Indexing (CBMI)

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“…In practice, this process pruned approximately 35% to 40% of the frames from each document, which typically corresponded to non-topical or disfluent speech. Segmentation: After pruning, the remaining sequence of frames is segmented using a minimum cut algorithm as described in [11]. For the segmentation procedure, the similarity between frames fi and fj is calculated using the cosine similarity measure between their corresponding vectors of topical scores, si and sj.…”

Section: Windowingmentioning

confidence: 99%

Topic identification based extrinsic evaluation of summarization techniques applied to conversational speech

Harwath

Hazen

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Document summarization algorithms are most commonly evaluated according to the intrinsic quality of the summaries they produce. An alternate approach is to examine the extrinsic utility of a summary, measured by the ability of the summary to aid a human in the completion of a specific task. In this paper, we use topic identification as a proxy for relevancy determination in the context of an information retrieval task, and a summary is deemed effective if it enables a user to determine the topical content of a retrieved document. We utilize Amazon's Mechanical Turk service to perform a large-scale human study contrasting four different summarization systems applied to conversational speech from the Fisher Corpus. We show that these results appear to be correlated with the performance of an automated topic identification system, and argue that this automated system can act as a low-cost proxy for a human evaluation during the development stages of a summarization system.

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“…Our work is also related to text segmentation (Ji et al, 2003) and meeting segmentation (Malioutov et al, 2006;Malioutov et al, 2007;Galley et al, 2003;Eisenstein et al, 2008). Text segmentation identifies boundaries of topic changes in long text documents, but we form threads of messages from streams consisting of short messages.…”

Section: Related Workmentioning

confidence: 99%

Context-based message expansion for disentanglement of interleaved text conversations

Wang

Oard

2009

Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Com

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Computational processing of text exchanged in interactive venues in which participants engage in simultaneous conversations can benefit from techniques for automatically grouping overlapping sequences of messages into separate conversations, a problem known as "disentanglement." While previous methods exploit both lexical and non-lexical information that exists in conversations for this task, the inter-dependency between the meaning of a message and its temporal and social contexts is largely ignored. Our approach exploits contextual properties (both explicit and hidden) to probabilistically expand each message to provide a more accurate message representation. Extensive experimental evaluations show our approach outperforms the best previously known technique.

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Minimum cut model for spoken lecture segmentation

Cited by 133 publications

References 32 publications

Comparing segmentation strategies for efficient video passage retrieval

Comparing segmentation strategies for efficient video passage retrieval

Topic identification based extrinsic evaluation of summarization techniques applied to conversational speech

Context-based message expansion for disentanglement of interleaved text conversations

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