The paper is dedicated to solving the problem of optimal text classification in the areaof automated detection of typology of texts. In conventional approaches to topicality-based textclassification (including topic modeling), the number of clusters is to be set up by the scholar, andthe optimal number of clusters, as well as the quality of the model that designates proximity oftexts to each other, remain unresolved questions. We propose a novel approach to the automateddefinition of the optimal number of clusters that also incorporates an assessment of word proximityof texts, combined with text encoding model that is based on the system of sentence embeddings.Our approach combines Universal Sentence Encoder (USE) data pre-processing, agglomerativehierarchical clustering by Ward’s method, and the Markov stopping moment for optimal clustering.The preferred number of clusters is determined based on the “e-2” hypothesis. We set up anexperiment on two datasets of real-world labeled data: News20 and BBC. The proposed model istested against more traditional text representation methods, like bag-of-words and word2vec, to showthat it provides a much better-resulting quality than the baseline DBSCAN and OPTICS models withdifferent encoding methods. We use three quality metrics to demonstrate that clustering quality doesnot drop when the number of clusters grows. Thus, we get close to the convergence of text clusteringand text classification.

No abstract

Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the models on various types of data, which brings only marginal improvements and does not lead to massive practical employment of the method. In particular, abstractive summarization is not used for social media research, where it would be very useful for opinion and topic mining due to the complications that social media data create for other methods of textual analysis. Of all social media, Reddit is most frequently used for testing new neural models of text summarization on large-scale datasets in English, without further testing on real-world smaller-size data in various languages or from various other platforms. Moreover, for social media, summarizing pools of texts (one-author posts, comment threads, discussion cascades, etc.) may bring crucial results relevant for social studies, which have not yet been tested. However, the existing methods of abstractive summarization are not fine-tuned for social media data and have next-to-never been applied to data from platforms beyond Reddit, nor for comments or non-English user texts. We address these research gaps by fine-tuning the newest Transformer-based neural network models LongFormer and T5 and testing them against BART, and on real-world data from Reddit, with improvements of up to 2%. Then, we apply the best model (fine-tuned T5) to pools of comments from Reddit and assess the similarity of post and comment summarizations. Further, to overcome the 500-token limitation of T5 for analyzing social media pools that are usually bigger, we apply LongFormer Large and T5 Large to pools of tweets from a large-scale discussion on the Charlie Hebdo massacre in three languages and prove that pool summarizations may be used for detecting micro-shifts in agendas of networked discussions. Our results show, however, that additional learning is definitely needed for German and French, as the results for these languages are non-satisfactory, and more fine-tuning is needed even in English for Twitter data. Thus, we show that a ‘one-for-all’ neural-network summarization model is still impossible to reach, while fine-tuning for platform affordances works well. We also show that fine-tuned T5 works best for small-scale social media data, but LongFormer is helpful for larger-scale pool summarizations.

The paper is devoted to the problem of modeling general-language frequency using data of large Russian corpora. Our goal is to develop a methodology for forming a consolidated frequency list which in the future can be used for assessing lexical complexity of Russian texts. We compared 4 frequency lists developed from 4 corpora (Russian National Corpus, ruTenTen11, Araneum Russicum III Maximum, Taiga). Firstly, we applied rank correlation analysis. Secondly, we used the measures “coverage” and “enrichment”. Thirdly, we applied the measure “sum of minimal frequencies”. We found that there are significant differences between the compared frequency lists both in ranking and in relative frequencies. The application of the “coverage” measure showed that frequency lists are by no means substitutable. Therefore, none of the corpora in question can be excluded when compiling a consolidated frequency list. For a more detailed comparison of frequency lists for different frequency bands, the ranked frequency list, based on RNC data, was divided into 4 equal parts. Then 4 random samples (containing 20 lemmas from each quartile) were formed. Due to the wide range of values, accepted by ipm measure, relative frequency values are difficult to interpret. In addition, there are no reliable thresholds separating high-frequency, mid-frequency, and low-frequency lemmas. Meanwhile, to assess the lexical complexity of texts, it is useful to have a convenient way of distributing lemmas with certain frequencies over the bands of the frequency list. Therefore, we decided to assign lemmas “Zipf-values”, which made the frequency data interpretable because the range of measure values is small. The result of our work will be a publicly accessible reference resource called “Frequentator”, which will allow to obtain interpretable information about the frequency of Russian words.

No abstract