527 research outputs found

    Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification

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    Connectionist temporal classification (CTC) is a powerful approach for sequence-to-sequence learning, and has been popularly used in speech recognition. The central ideas of CTC include adding a label "blank" during training. With this mechanism, CTC eliminates the need of segment alignment, and hence has been applied to various sequence-to-sequence learning problems. In this work, we applied CTC to abstractive summarization for spoken content. The "blank" in this case implies the corresponding input data are less important or noisy; thus it can be ignored. This approach was shown to outperform the existing methods in term of ROUGE scores over Chinese Gigaword and MATBN corpora. This approach also has the nice property that the ordering of words or characters in the input documents can be better preserved in the generated summaries.Comment: Accepted by Interspeech 201

    Testing Technicolor Models via Top Quark Pair Production in High Energy Photon Collisions

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    Pseudo-Goldstone boson contributions to ttˉt\bar{t} production rates in technicolor models with and without topcolor at the s=0.5and1.5\sqrt{s}=0.5 and 1.5 TeV photon colliders and hadron colliders are reviewed. For reasonable ranges of the parameters, the contributions are large enough to be experimentally observable. Models with topcolor, without topcolor and the MSSM with tanβ1\tan\beta\geq 1 can be experimentally distinguished.Comment: Talk given by H.Y. Zhou at the III International Conference on Hyperons,Charm and Beauty Hadrons,Genova,Italy,June 30-July 3 199
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