Context-Aware Hybrid Text Summarization System With Customizable Summary Length

The With increased volumes of digital documents, it can be difficult to identify the key points within a document. The aim of this project is to create a context aware hybrid text summarization system with adjustable summary length. The system uses both TextRank based extractive methods and Transformer based abstractive methods. TextRank will be used to extract the most important sentences, before passing them into either a BART, PEGASUS or T5 model. During refinement, the summary should be reduced with little repetition whilst maintaining the original context. Using a Streamlit interface the user will be able to choose from short, medium or detailed summaries. From reviewing the project design, it was identified that Python would be used along with Natural Language Processing libraries and SQLite. This paper will outline the architecture, methodology and reproducible evaluation methodology using ROUGE.