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The article addresses the challenge of acquiring professional economic discourse among students pursuing a bachelor’s degree in economics. This institutional economic discourse is not merely a collection of terminological units but a complex and evolving system that exhibits a broad spectrum of genre, stylistic, and pragmatic relevance. The discourse in the economic field represents the interconnections of knowledge, various language units, and their meanings. In efforts to comprehend and elucidate economic phenomena, its practitioners employ diverse forms through which economic concepts are transformed into knowledge within an educational context. The study aims to identify effective methods for studying English-language economic discourse for students who have chosen economics as their future career. The authors utilize examples of non-codified language units from economic discourse and compared online machine-translated examples with translations produced by learners as empirical material for this research. Translating economic texts from English to Russian, editing texts translated by online translators, summarizing in English, and engaging in follow-up discussions emerged as the most effective strategies for learners to establish themselves as agents of economic discourse. These techniques ensure the comprehensive establishment, maintenance, and advancement of discourse practices. The study employs observation and analysis, logical juxtaposition, and the component analysis method. The authors conclude that the aforementioned methods for acquiring economic discourse should be given priority. This approach significantly enhances the effectiveness of converting acquired knowledge into communicative practices, transforming the discourse of the economic sphere into a dynamic tool for recreating professional expertise, with a high level of involvement from potential discourse agents in the acquisition process.

Generative text summarization can condense large volumes of information into a concise summary. It helps users quickly grasp the main points of a text without having to read the entire document. Machine learning (ML) plays a pivotal role in this domain, offering significant advantages in information processing and comprehension. In this paper we present CoMod, an abstractive method for generating the context of a document, from its content and that of the referenced documents, if any. CoMod analyzes the intricate patterns and relationships within a document’s content, thereby extracting and inferring the underlying context. The context generation process involves using a word linearization process as well as a Markov model, specifically a Bigram model, to predict the likelihood of word sequences. The Markov model is trained on a corpus of text and used to generate coherent sentences based on the probabilities of transitioning from one word to another. Markov tables allows to adapt the generated context to a specific domain and can be built on the fly in CoMod. The approach was compared to other methods and demonstrated very encouraging capabilities by outperforming other approaches tested on the same datasets. It thus confirms the potential of generative methods in the field of automatic text summarization and their ability of leveraging the power of machine learning for context generation to revolutionize information management, boosting productivity, scalability and knowledge discovery in various domains.

Moroccan internauts tend these days to use a very new specific language, a new trend of communication on the web, mainly on social media, to express their opinions and exchange their ideas, and that what we greed to call the Moroccan sociolect language given it extensive use by almost all Moroccan netizens. Since our final purpose is to be able to analyze the opinions of these internauts community, it is mandatory to first understand the language they are using to communicate on the web, thus, in this paper, we present in details the specific features of this Moroccan sociolect language, also we propose a set of steps, models and algorithms to follow in order to understand and summarize a Moroccan sociolect text. [ABSTRACT FROM AUTHOR]

The aim of this article is to describe an existing implementation of a text summarizer forPolish, to analyze the results and propose the possibilities of further development. Theproblem of text summarizing has been already addressed by science but until now there hasbeen no implementation designed for Polish. The implemented algorithm is based on existingdevelopments in the field but it also includes some improvements. It has been optimized fornewspaper texts ranging from approx. 10 to 50 sentences. Evaluation has shown that it worksbetter than known generic summarization tools when applied to Polish.