Keyphrase Prediction (KP) task aims at predicting several keyphrases that can summarize the main idea of the given document. Mainstream KP methods can be categorized into purely generative approaches and integrated models with extraction and generation. However, these methods either ignore the diversity among keyphrases or only weakly capture the relation across tasks implicitly. In this paper, we propose UniKeyphrase, a novel end-to-end learning framework that jointly learns to extract and generate keyphrases. In UniKeyphrase, stacked relation layer and bagof-words constraint are proposed to fully exploit the latent semantic relation between extraction and generation in the view of model structure and training process, respectively. Experiments on KP benchmarks demonstrate that our joint approach outperforms mainstream methods by a large margin. * Equal contribution. Document:On selecting an optimal wavelet for detecting singularities in traffic and vehicular data. …… applications of wavelet transform s ( wts ) in traffic engineering have been introduced however , …… , second order difference , oblique cumulative curve , and short time fourier transform ) . it then mathematically describes wts ability to detect singularities in traffic data . …… , it is shown that selecting a suitable wavelet largely depends on the specific research topic , and that the mexican hat wavelet generally gives a satisfactory performance in detecting singularities in traffic and vehicular data .

Keyphrase Prediction (KP) task aims at predicting several keyphrases that can summarize the main idea of the given document. Mainstream KP methods can be categorized into purely generative approaches and integrated models with extraction and generation. However, these methods either ignore the diversity among keyphrases or only weakly capture the relation across tasks implicitly. In this paper, we propose UniKeyphrase, a novel end-to-end learning framework that jointly learns to extract and generate keyphrases. In UniKeyphrase, stacked relation layer and bagof-words constraint are proposed to fully exploit the latent semantic relation between extraction and generation in the view of model structure and training process, respectively. Experiments on KP benchmarks demonstrate that our joint approach outperforms mainstream methods by a large margin. * Equal contribution. Document:On selecting an optimal wavelet for detecting singularities in traffic and vehicular data. …… applications of wavelet transform s ( wts ) in traffic engineering have been introduced however , …… , second order difference , oblique cumulative curve , and short time fourier transform ) . it then mathematically describes wts ability to detect singularities in traffic data . …… , it is shown that selecting a suitable wavelet largely depends on the specific research topic , and that the mexican hat wavelet generally gives a satisfactory performance in detecting singularities in traffic and vehicular data .

Generating factual-consistent summaries is a challenging task for abstractive summarization. Previous works mainly encode factual information or perform post-correct/rank after decoding. In this paper, we provide a factual-consistent solution from the perspective of contrastive learning, which is a natural extension of previous works. We propose CO2Sum (Contrastive for Consistency), a contrastive learning scheme that can be easily applied on sequence-to-sequence models for factual-consistent abstractive summarization, proving that the model can be fact-aware without modifying the architecture. CO2Sum applies contrastive learning on the encoder, which can help the model be aware of the factual information contained in the input article, or performs contrastive learning on the decoder, which makes the model to generate factualcorrect output summary. What's more, these two schemes are orthogonal and can be combined to further improve faithfulness. Comprehensive experiments on public benchmarks demonstrate that CO2Sum improves the faithfulness on large pre-trained language models and reaches competitive results compared to other strong factual-consistent summarization baselines.

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