State-of-the-Art Approach to e-Learning with Cutting Edge NLP Transformers: Implementing Text Summarization, Question and Distractor Generation, Question Answering

Abstract

Amid the worldwide wave of pandemic lockdowns, there has been a remarkable growth in E-learning. Online learning has become a challenge for students. It has become difficult for students to find the content they need. The mounting accessibility of textual content has necessitated comprehensive study in the areas of automatic text summarization and question generation. Multiple Choice Questions is very smooth for evaluations, and its assessment is implemented through computerized applications in order that results may be declared within some hours, and the evaluation system is 100% pure. The system proposes an interactive reading platform where the user can upload an E-Book and get textual summary and generates questions like MCQs, fill in the blanks and one word. The user can also evaluate the questions answered. The proposed system is an all-in-one interactive reading platform.

Question Answer System: A State-of-Art Representation of Quantitative and Qualitative Analysis

“…The similarity measures like Jaro-Vinkler [101], UMBC+LSA [104], and Siamese LSTM [121] have also been used for mapping the entities. Few QA systems have also used approaches based on NN [122], Hierarchical RNN [16], BiLSTM [123], and BERT [124,125].…”

Multimodal Abstractive Summarization using bidirectional encoder representations from transformers with attention mechanism

“…These summaries can be used further for auto captioning, annotation purpose which makes search and retrieval process easier. In general, there are two categories for summarization tasks: extractive and abstractive method [ [9] , [10] ]. In extractive summarization key information of input are extracted and combined together to form summary while in abstractive summarization, summary is generated by understanding semantic representation of input [ 11 ].…”