Optimized summarization of research papers using data mining strategies

Abstract

Whenever a research scholar starts working on some innovative ideas he/she searches for the domain specific technical research articles published as research papers in most of the international journals, conferences or workshops. Typical information retrieval (IR) or digital library systems make these related research papers available as electronic text documents by ranking them based on their relevance to the user query. The problems associated with these papers are similarity in contents and the repeated related information. Thus reading them all completely one by one to get the latest research developments in the interested domain is time-consuming, unnecessary, irrelevant, cumbersome and impossible. newlineWe have solved these problems by developing innovative method, which automatically optimizes and summarizes research papers extracted through search engines as an aid for research scholars using Data Mining strategies. Thus helping research scholar for getting short, condensed, accurate and most related summarized information reporting latest research developments in the interested field i.e. overview of domain-specific topic-based multiple research papers. newlineA lot of research work has been done in this area focusing generic or user oriented needs. However, despite extensive research, summarizing multiple domain-specific topic-driven related documents whose contents depend on the preference of the user query and making the final summary focused on a particular area are one of the major challenges in current summarization tasks. newlineIn addition to optimized and summarized contents from multiple research papers, this theory informs earlier and latest research developments, progress, challenges and future scope in particular field of study. The idea is to specify research methods/techniques/approaches used, compared with others, providing starting material for further innovation. newline