In the age of social media, faced with a huge amount of knowledge and information, accurate and effective keyphrase extraction methods are needed to be applied in information retrieval and natural language processing. It is difficult for traditional keyphrase extraction models to contain a large amount of external knowledge information, but with the rise of pre-trained language models, there is a new way to solve this problem. Based on the above background, we propose a new baseline for unsupervised keyphrase extraction based on pre-trained language model called SIFRank. SIFRank combines sentence embedding model SIF and autoregressive pre-trained language model ELMo, and it has the best performance in keyphrase extraction for short documents. We speed up SIFRank while maintaining its accuracy by document segmentation and contextual word embeddings alignment. For long documents, we upgrade SIFRank to SIFRank+ by position-biased weight, greatly improve its performance on long documents. Compared to other baseline models, our model achieves state-of-the-art level on three widely used datasets. INDEX TERMS Keyphrase extraction, pre-trained language model, sentence embeddings, position-biased weight, SIFRank. I. INTRODUCTION Keyphrase extraction is the task of selecting a set of words or phrases from a document that could summarize the main topics discussed in the document [1]. Keyphrase extraction can greatly accelerate the speed of information retrieval, help people get the first-hand information from a long text quickly and accurately. A. MOTIVATION Keyphrase Extraction can be divided into two main kinds of approaches: supervised and unsupervised. Supervised methods perform better on specific domain tasks, but it takes a lot of labor to annotate the corpus, and the model after training may overfit and do not work well on other datasets. The main traditional unsupervised methods are mainly divided into the models based on statistics and the models based on The associate editor coordinating the review of this manuscript and approving it for publication was Shuai Han .

Using the associativity relations of the topological Sigma Models with target spaces, CP 3 , CP 4 and Gr(2, 4) , we derive recursion relations of their correlation and evaluate them up to certain order in the expansion over the instantons. The expansion coeffieients are regarded as the number of rational curves in CP 3 , CP 4 and Gr(2, 4) which intersect various types of submanifolds corresponding to the choice of BRST invariant operators in the correlation functions.

Studies based on the analysis of a new design of loyalty program, item-based loyalty programs (IBLPs), indicate that customers are more interested in item-based reward points than in traditional price discounts. However, we are still unaware of customer responses to the different point settings on IBLP items. This study investigates an analysis with Tobit II to explore IBLPs’ short-term (4 months) impact on customers’ purchase behaviors using data from two newly opened Japanese supermarket chains that have implemented this new IBLP program from the beginning. The results showed that different types of customers are differently affected by IBLPs, and that heavy customers are more inclined to purchase more items with more spending money than others. The results also indicated that customers’ purchase behaviors are affected by IBLPs’ different point levels. Moreover, to an IBLP with different points, the responses from different types of customers are different. The findings of this study have important guiding significance in IBLP design and marketing management.

We consider a complex scalar Φ 4 theory with spontaneously broken global U(1) symmetry, minimally coupling to perturbatively quantized Einstein gravity which is treated as an effective theory at the energy well below the Planck scale. Both the lowest order pure real scalar correction and the gravitational correction to the renormalization of the Higgs sector in this model have been investigated. Our results show that the gravitational correction renders the renormalization of the Higgs sector in this model inconsistent while the pure real scalar correction to it leads to a compatible renormalization.

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