Extractive Text Summarization using Dynamic Clustering and Co-Reference on BERT

The process of picking sentences directly from the story to form the summary is extractive summarization. This process is aided by scoring functions and clustering algorithms to help choose the most suitable sentences. We use the existing BERT model which stands for Bidirectional Encoder Representations from Transformers, to produce extractive summarization by clustering the embeddings of sentences by K-means clustering, but introduce a dynamic method to decide the suitable number of sentences to pick from clusters.On top of that, the study is aimed at producing summaries of higher quality by incorporating reference resolution and dynamically producing summaries of suitable sizes depending on the text. This study aims to provide students with a summarizing service to help understand the content of lecture videos of long duration which would be vital in the process of revision.