Automatic Abstracting

Course: «Applied Linguistics (Translation Editing and Expert Linguistic Analysis)»

Structural unit: Educational and Scientific Institute of Philology

Title
Automatic Abstracting
Code
ДВС.1.10
Module type
Вибіркова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
4 Semester
Number of ECTS credits allocated
2
Learning outcomes
PLO 11. To perform a scholarly analysis of linguistic, speech, and literary material, interpret and structure it considering expedient methodological principles, and formulate summarizations based on the individually processed data. PLO 16. To use specialized conceptual knowledge in applied linguistics to solve complex tasks and issues, which requires updates and integration of knowledge, often under conditions of incomplete/lacking information and contradictory requirements. PLO 17. To plan, organize, perform, and present the research and or innovative developments in an applied linguistics field. PLO 20.1. To apply the methods of designing dialogue systems (question-answer system), systems of speech recognition and synthesis, automatic editing and text retrieval, content analysis, and sentiment analysis in the creation of linguistic information technologies. PLO 22.1.
Form of study
Full-time form
Prerequisites and co-requisites
Before starting this course, students should know the main stages of automatic text analysis and have basic factual knowledge of a linguistic nature. Be able to collect and interpret information about linguistic phenomena; single out the features of the products of speech activity necessary for the automation of text analysis, apply the organization of the language system in speech analysis; plan and evaluate own work; use interactive and multimedia tools. Have elementary skills in scientific research and information management; critical attitude to the analyzed phenomena; use of foreign language professional informative sources; works with linguistic texts; production of complex oral and written messages; interaction and cooperation in learning in exploratory situations.
Course content
The purpose of the discipline is to form knowledge about the history and current state of automatic text referencing, develop skills and develop the skills of referencing using methods of grid modeling of vocabulary, and functional weight of words and sentences to solve analytical and research tasks for processing scientific and journalistic texts. Its subject is the study of methods of automatic abstracting of texts, history, and the state of development of the problem, mastering the skills of working with existing systems and acquiring the skills of abstracting using various methods (network modeling of vocabulary; the functional weight of words and sentences in the text) in order to form the necessary professional competences of students, future specialists: editors, translators and experts capable of solving complex specialized tasks and practical problems, implementation of analytical and prognostic research and development of information policy strategies.
Recommended or required reading and other learning resources/tools
1. CLARIN (Common Language Resources and Technology Infrastructure). URL: http://clarin-pl.eu/index.php/en/home/ 2. MS Office AutoSummarize. URL: http://surl.li/ediqi 3. Summarizer. URL: https://www.paraphraser.io/text-summarizer 4. Summary Box. URL: http://surl.li/ediqh
Planned learning activities and teaching methods
Lectures, laboratory, and practical classes, independent work.
Assessment methods and criteria
Assessment of semester work: 1. Oral response, additions, participation in lectures and practical discussions, practical tasks, tests, presentation defense: 48/80 points. 2. Control test 12/20 points. Points received during the semester for the discipline Part 5 "Automatic abstracting" are transferred in percentage equivalent (50% of the points received) to the formation of the total score for the two parts of the complex discipline "Linguistic aspects of AOPM systems" (Part 1 "Automatic content and sentiment analysis" + Part 5 "Automatic abstracting".
Language of instruction
Ukrainian, English

Lecturers

This discipline is taught by the following teachers

Departments

The following departments are involved in teaching the above discipline