Justin Jian Zhang

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We present the first known empirical study on speech summarization without lexical features for Mandarin broadcast news. We evaluate acoustic, lexical and structural features as predictors of summary sentences. We find that the summarizer yields good performance at the average Fmeasure of 0.5646 even by using the combination of acoustic and structural(More)
We propose a novel method of extractive summarization of lecture speech based on unsupervised learning of its rhetorical structure. We present empirical evidence showing that rhetorical structure is the underlying semantics which is then rendered in linguistic and acoustic/prosodic forms in lecture speech. We present a first thorough investigation of the(More)
Previous studies have indicated that intracerebroventricular (i.c.v.) infusions of corticotropin-releasing factor (CRF) activate locus coeruleus (LC) noradrenergic neurons and increase the metabolism and extracellular concentrations of norepinephrine (NE) in several brain regions, suggesting increased release. To examine the temporal aspects and mechanism(More)
We propose an extractive summarization approach with a novel shallow rhetorical structure learning framework for speech summarization. One of the most under-utilized features in extractive summarization is hierarchical structure information-semantically cohesive units that are hidden in spoken documents. We first present empirical evidence that rhetorical(More)
In this paper, we propose a one step rhetorical structure parsing, chunking and extractive summarization approach to automatically generate meeting minutes from parliamentary speech using acoustic and lexical features. We investigate how to use lexical features extracted from imperfect ASR transcriptions, together with acoustic features extracted from the(More)
We show a novel approach of automatically generating minutes style extractive summaries for parliamentary speech. Minutes are structured summaries consisting of sequences of business items with sub-summaries. We propose to model minute structures as a rhetorical syntax tree. We also propose to use a single Conditional Random Field classifier to carry out(More)
We propose an extractive summarization system with a novel non-generative probabilistic framework for speech summarization. One of the most underutilized features in extractive summarization is rhetorical information - semantically cohesive units that are hidden in spoken documents. We propose Rhetorical-State Hidden Markov Models (RSHMMs) to automatically(More)
This paper introduces our work on mandarin lecture speech transcription. In particular, we present our work on a small database, which contains only 16 hours of audio data and 0.16M words of text data. A range of experiments have been done to improve the performances of the acoustic model and the language model, these include adapting the lecture speech(More)
We present an assembly cell consisting of two cooperating robots and a variety of sensors. It offers a number of complex skills necessary for constructing aggregates from elements of a toy construction set. A high degree of flexibility was achieved because the skills were realised only through sensory feedback, not by resorting to fixtures or specialised(More)