Text Summarisation Using BERT

Artificial intelligence has significantly increased during the last ten years. AI-related text summarisation, which identifies the pertinent sentences from a text, is a significant research topic. Text summarisation may obtain concise and accurate information while maintaining the text’s original structure. The method for creating a succinct and accurate extraction summary for the provided text material is presented in this project. This paper provides a comprehensive literature review to gauge the complexities in the existing solutions, followed by the proposed solution using BERT for finding embeddings. As a classification challenge, extractive summarisation is done by predicting a class for each sentence in a document (i.e. determining if a sentence should or should not be a part of the generated summary). The sentences are assigned a rank, also known as their prediction score. The summary is then provided by ordering the best-scoring sentences according to certain relevant factors (e.g. order of appearance in the document, grammatical correctness, etc.). The news articles from CNN/Daily mail are used to verify the accuracy.