期刊名称：International Journal of Early Childhood Special Education
电子版ISSN：1308-5581
出版年度：2022
卷号：14
期号：2
页码：2983-2993
DOI：10.9756/INT-JECSE/V14I2.290
语种：English
出版社：International Journal of Early Childhood Special Education
摘要：Manual transcription and summarization is a cumbersome process necessitating the development of an efficient automatic text summarization technique. In this study, a Chrome extension is used for making the process of transcription hassle free. It uses the text summarization technique to generate concise and succinct matter. Also, the tool is accessorized using Google Translation, to convert the processed text into users' desired language. This paper illustrates, how captions can be traced from the online meetings, corresponding to which, meeting transcript is sent to the backend where it is summarized using an NLP model. It also walks through three different NLP models and presents a comparative study among them. The NLTK model utilizes the sentence ranking technique for extractive summarization. Word Embedding model uses pre -trained Glove Embeddings for extractive summarization. The T5 model performs abstractive summarization using transformer architecture. The working of the model is tested over meeting texts taken from various sources and results show that the NLTK model has an edge over the Word Embedding model based on ROUGE-1, ROUGE-2, and ROUGE-L scores. However, our analysis finds that T5 is generating a more concise summary.
关键词：text summarization;precision f measure;automatic text summarization;word embedding model;f measure recall;measure recall precision;natural language processing;automatically generated summary;abstractive summarization;vector representation;extrac
