# Y-Rank: A Multi-Feature-Based Keyphrase Extraction Method for Short Text
## 1 Introduction  
As the development of big data era, the large amount of Web and social media data has grown exponentially. For example, Wikipedia [1] has more than 5,836,552 articles and more than 27 billion words. The increasing amount of textual data poses a significant challenge to the task of document retrieval, which has led to an urgent need for effective methods in processing large-scale text datasets. As the core sub-task in document retrieval, keyphrase extraction refers to the extraction of a set of meaningful phrases from documents automatically. Keyphrase extraction is a versatile approach and can be applied to a large number of downstream tasks, such as automatic question-answering systems, recommendation systems, sentiment analysis, etc. For example, [PERSON] [2] proposed the common-sense QA system, which extracted keyphrases from a large number of documents as prior knowledge. [PERSON] [3] proposed a method that includes both embed keyphrases and user-item preferences, and a closed-end Bayesian method based on keyphrase feedback, which identifies user-item preferences in the interactive recommendation system according to the preferences induced in the natural language space. In addition, keyphrases can be used not only as features for document clustering and classification [4], but also as topic words for automatic document summarization [5]. The traditional keyphrase extraction methods can be categorized into supervised and unsupervised. Supervised methods have powerful modeling ability and usually have higher accuracy than unsupervised methods, but also have defects, such as dependence on well-labeled training corpuses, huge training costs by manual labeling, subjectivity problems, and a lack of interpretability. Compared with supervised methods, unsupervisedmethods have less dependence on labeled training data, and are domain-independent and interpretable. However, the accuracy of existing unsupervised methods is relatively low, which cannot adapt to the growing demand for data processing. Therefore, in recent years, the optimization of unsupervised methods has become a hot issue in the field of information retrieval. Based on the relevance of documents, the text features by traditional unsupervised keyphrase extraction methods can be roughly classified into two categories: cross-document features and single-document features. The common cross-document features are document embedding, document frequency, etc, which denote the similarity between documents. When documents are related to each other, cross-document features can effectively improve the accuracy of keyphrase extraction. However, when documents are dissimilar and independent of each other, the traditional metrics always lead to the cross-document features degeneration as single-document features, such as the frequency of words, term co-occurrence, etc. Compared with cross-document features, the unsupervised approach, which improves the accuracy by adding effective single-document features, is more general. Although scholars have come up with a large number of semantic and statistical models, the accuracy is still unsatisfactory. [PERSON] and [PERSON] [6] found that semantic features and statistical features have different effects in different text lengths, for example, the semantic features are more reliable in the context of short-text or medium-text, while statistical features are more robust in long documents (e.g., more than 1000 words). All in all, how to search more short-text features (semantic, statistical, etc.) to improve the accuracy metrics has become a major concern for researchers in the related field. Inspired by these observations, this paper proposes Y-Rank, an unsupervised keyphrase extraction method for short text in single document scenarios. Y-Rank takes into account both semantic and statistical features, and avoids the problem of phrases that are semantically similar but have sparse co-occurrence in traditional graph construction methods.