Ettekannete automaatne sessioonideks jagamine teaduskonverentside jaoks

Abstract

The aim of this work is to create a method, using a language model, that can divide the articles accepted for a scientific conference into sessions by topic, so that participants can listen to a succession of presentations on a similar topic. In developing the method, it is necessary to know in advance the problem of scheduling a conference, the principles of creating a good session title, and the principles of creating story prompts. The titles and abstracts of scientific articles will be provided for the method. Based on this data, the language model is queried to generate possible session titles, followed by the use of prompts to generalise the titles. Occurring titles are reviewed to ensure their suitability in the context of a machine learning conference by removing low-value titles. The remaining titles are evaluated with a language model according to the content and title of each presentation, followed by a segmentation into sessions using a linear integer optimization algorithm. The process is completed by using Levenshtein distance to estimate the similarity of the segmentation of the sessions.