Comparative Analysis of NLP models for Google Meet Transcript Summarization

EasyChair Preprint 5404

Abstract

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.

Keyphrases: Abstractive Summarization, Extractive Summarization, GloVe, NLP, NLTK, Natural Language Processing, ROUGE, T5, measure recall precision, meet transcript, model summary, word embedding

@booklet{EasyChair:5404,
  author    = {Yash Agrawal and Atul Thakre and Tejas Tapas and Ayush Kedia and Yash Telkhade and Vasundhara Rathod},
  title     = {Comparative Analysis of NLP models for Google Meet Transcript Summarization},
  howpublished = {EasyChair Preprint 5404},
  year      = {EasyChair, 2021}}