Text summarization and question-answer generator for real time transfer of media files using websockets with BERT and DistilBERT algorithm.

The use of the learning is to perform the text summarization capabilities of DistilBERT algorithm and compare it with the BERT algorithm and analyze the accuracy and the performance metrics of both the Algorithms. Text Summarization and Question - Answer generation is performed by using the pre trained models in the famous python transformers library. The corpus of at least 10-20 pages is taken and the operations are performed on that corpus. In the same way, 30 sets of the corpus are taken and they are divided into two groups. The two groups were BERT and DistilBERT algorithms. A comparison was made between the two algorithms' performance in terms of speed and accuracy. After calculating the means, standard deviations, as well as standard error means, we used the T-test for independent samples to see whether there was a statistically significant distinction between the groups. The significance level of P 0.001 (2-tailed) indicates a statistically significant difference between DistilBERT and BERT. The study proved that DistilBERT achieved better accuracy in a shorter amount of time compared to the BERT algorithm. Study shows that DistilBERT is more performant than BERT with fewer parameters. [ABSTRACT FROM AUTHOR]