Automated Summarization Tool

Abstract

As the size of file is increasing in non-structured text format in academic, corporate, law and organisation, there is a severe need of information-extraction and text-summarization systems. Doing summary manually is a time consuming, inconsistent and unscalable process. This paper presents the design realization and evaluation of an Automated Summarization Tool (AST) which is a document intelligence platform based on google gemini 2.5 flash. The platform employs map-reduce summarization for long documents, the use of SHA-256 hash for caching, a RAG-lite chat module grounded in the source document enabling conversational chats, role- and toneadaptive prompt engineering, and a JSON-based output schema coupling every extracted key point with a verbatim quote and location from the source for traceability, thereby preventing unnecessary API calls. The complete system features a Gradio web interface. It is deployed as a zero-infrastructure Google Colab notebook. Thus, no dedicated server or installation is required. In internal, multi-domain test corpora, our platform achieves a ROUGE-1 score of 58.17. This is the highest we obtain out of six different summarization systems with which we compare against other methods. And it outperforms the strongest fine-tuned transformer baselines (PEGASUS, BART) by ~14 points. And it is clearly ahead of BERTSUM-ext (a strong transformer baseline), Pointer-Generator Network, TextRank.