Analysis of Extractive Text Summarisation Techniques for Multilingual Texts

Due to a large influx of information around us, text summarization is becoming increasingly important. Text summary helps one understand the document without having to read every line of the stack of papers. Text summarization provides a method for creating a condenses version of single or multiple articles at hand. It is an important technique for locating relevant content on the Internet or in sizable text libraries. It is also crucial to extract the information so that the user will find the article interesting. Text summarization may be performed using two methods - extractive techniques and abstractive techniques. Dividing the text into sentences and selecting the best ones based on their weight or score is a method to produce extractive summary. The essential concepts of the article are retrieved using the abstractive summarization approach, which then creates a subjective, natural-language representation of those ideas. In this paper, the goal is to provide a thorough analysis of extractive text summarizing process techniques and to provide an in-depth interpretation of extractive text summarization techniques with a summary that is less repetitious and has a high degree of coherence. A comprehensive comparison research of several extractive approaches for automated text summarization on Hindi, English and French text is presented in this work.