Papers' similarity based on the summarization merits

摘要

This paper proposes a Research paper Similarity system that measures the similarity of an input paper with other papers based on the summarized version of each paper. Currently, This system will take into account 2 different types of summarization for papers based on the different types of keywords,i.e, Normal keywords and Stemmed keywords. On the contrast to the current and existing recommendation systems for research papers that are using citation and/or Page Rank data, our system works dependent from them but dependent to the textual content of the paper. Our experiment, which was conducting regarding to one of the citation-based papers' recommendation systems, Google Scholar, as a baseline, shows that citation-based systems like Google scholar are very vulnerable to ignore more related but less cited papers while systems based on the textual value of papers can be more successful to recommend papers that are more similar to the input paper. However, comparing full-textual content of papers is a time consuming and aggressive process, while achieving a summarized version of papers and comparing them, can be both faster and reusable. In addition, we show that the ranked listing that Google scholar returns, can be formulated and predicted based on the citation scores. Furthermore, we show that how statistically, Normal keyword summarization can be a better choice between the two types of summarization of papers. As a future work, we will build a synonym-acronym dictionary for scholarly papers in computer science and engineering field, to add the synonym-acronym comparison to the system.