Methodology for Automatic Information Extraction and Summary Generation from Online Sources for Project Funding

The summarized content of one or more extensive text documents helps users extract only the most important key information, instead of reviewing and reading hundreds of pages of text.This study uses extractive and abstractive mechanisms to automatically extract and summarize information retrieved from various web documents on the same topic.The research aims to develop a methodology for designing and developing an information system for pre-and post-processing natural language obtained through web content search and web scraping, and for the automatic generation of a summary of the retrieved text.The research outlines two subtasks.As a first step, the system is designed to collect and process up-to-date information based on specific criteria from diverse web resources related to project funding, initiated by various organizations such as startups, sustainable companies, municipalities, government bodies, schools, the NGO sector, and others.As a second step, the collected extensive textual information about current projects and programs, which is typically intended for financial professionals, is to be summarized into a shorter version and transformed into a suitable format for a wide range of non-specialist users.The automated AI software tool, which will be developed using the proposed methodology, will be able to crawl and read project funding information from various web documents, select, process, and prepare a shortened version containing only the most important key information for its clients.