Keyphrases are crucial for searching and systematizing scholarly documents. Most current methods for keyphrase extraction are aimed at the extraction of the most significant words in the text. But in practice, the list of keyphrases often includes words that do not appear in the text explicitly. In this case, the list of keyphrases represents an abstractive summary of the source text. In this paper, we experiment with popular transformer-based models for abstractive text summarization using four benchmark datasets for keyphrase extraction. We compare the results obtained with the results of common unsupervised and supervised methods for keyphrase extraction. Our evaluation shows that summarization models are quite effective in generating keyphrases in the terms of the full-match F1-score and BERTScore. However, they produce a lot of words that are absent in the author’s list of keyphrases, which makes summarization models ineffective in terms of ROUGE-1. We also investigate several ordering strategies to concatenate target keyphrases. The results showed that the choice of strategy affects the performance of keyphrase generation.

Text complexity assessment is a challenging task requiring various linguistic aspects to be taken into consideration. The complexity level of the text should correspond to the reader’s competence. A too complicated text could be incomprehensible, whereas a too simple one could be boring. For many years, simple features were used to assess readability, e.g. average length of words and sentences or vocabulary variety. Thanks to the development of natural language processing methods, the set of text parameters used for evaluating readability has expanded significantly. In recent years, many articles have been published the authors of which investigated the contribution of various lexical, morphological, and syntactic features to the readability level. Nevertheless, as the methods and corpora are quite diverse, it may be hard to draw general conclusions as to the effectiveness of linguistic information for evaluating text complexity due to the diversity of methods and corpora. Moreover, a cross-lingual impact of different features on various datasets has not been investigated. The purpose of this study is to conduct a large-scale comparison of features of different nature. We experimentally assessed seven commonly used feature types (readability, traditional features, morphological features, punctuation, syntax frequency, and topic modeling) on six corpora for text complexity assessment in English and Russian employing four common machine learning models: logistic regression, random forest, convolutional neural network and feedforward neural network. One of the corpora, the corpus of fiction literature read by Russian school students, was constructed for the experiment using a large-scale survey to ensure the objectivity of the labeling. We showed which feature types can significantly improve the performance and analyzed their impact according to the dataset characteristics, language, and data source.

In this paper, we attempted to adapt various well-known algorithms for keyword selection to a very specific text corpus containing abstracts of Russian academic papers from the mathematical and computer science domain. We faced several challenges including the lack of research in the field of keyword extraction for Russian, the absence of large text corpora of academic abstracts, and the insufficient length of the abstracts. Keywords are often found in the full text of the paper and can simply be highlighted, whereas abstracts may not include keywords in an explicit form. At the same time, it is abstracts that are usually in the public domain, so automatic selection of keywords from them would significantly facilitate the process of searching for papers. Moreover, an automatic keyword selection would be useful even for papers for which keywords were already specified by the authors. During the study, we found that authors often use unique keywords for their papers. This complicates their systematization on a given topic. For visualizing the results, we have created a web resource keyphrases.mca.nsu.ru, where young/beginning scholars can form an approximate list of keywords for their first research paper.