An Efficient Summarisation and Search Tool for Research Articles.

Building an efficient summarization and search tool for research articles is a complex task that involves interdisciplinary expertise in NLP, database management, web development, and user experience design. With the rapid growth of the scientific content, manually reading and selecting important content of research articles became challenging. Thus, there is a need for a summarization tool to help scholars reading their content fast along with a search tool that will find important contents and keep them in organized way. To summarize the contents of different articles a summarization tool is proposed in this work that generates extractive and abstractive summaries. Along with summarization a search engine also been proposed in this work that save the searched results in a comma-separated value (CSV) format including the search queries and meta information of articles such as keyword, title, author name, URL, year of publication, abstracts and summaries. These CSVs help users to get idea about article contents in offline mode without reading or searching the whole text. The efficiency of the summarizer tool is evaluated in terms of precision(pr), recall(re) and F-measure(F-m) of Rouge-1(R1), Rouge-2(R2), Rouge_sum(R_sum) and Bertscore(BS) measures for ten research articles. The average pr, re and F-m obtained from BS are 42%, 42% and 42% for extractive summarization and 41%, 41% and 41% for abstractive summarization. This tool will be helpful to research scholars in the collection of literature and the preparation of related work for their research. [ABSTRACT FROM AUTHOR]