Jia-Wen Liu

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The purpose of extractive speech summarization is to automatically select a number of indicative sentences or paragraphs (or audio segments) from the original spoken document according to a target summarization ratio and then concatenate them to form a concise summary. Much work on extractive summarization has been initiated for developing machine-learning(More)
Many of the existing machine-learning approaches to speech summarization cast important sentence selection as a two-class classification problem and have shown empirical success for a wide variety of summarization tasks. However, the imbalanced-data problem sometimes results in a trained speech summarizer with unsatisfactory performance. On the other hand,(More)
Aziridine-containing compounds have been of interest as anticancer agents since late 1970s. The design, synthesis and study of triaziquone (TZQ) analogues with the aim of obtaining compounds with enhanced efficacy and reduced toxicity are an ongoing research effort in our group. A series of bis-type TZQ derivatives has been prepared and their cytotoxic(More)
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