Research Paper Summarization and Recommendation

The exponential growth of academic literature has made it increasingly challenging for researchers to keep up with the latest developments in their respective fields. This paper presents an approach for summarizing and recommending research papers to help researchers efficiently manage and navigate the vast amount of academic literature. We propose using a transformer-based model for generating abstractive summaries of research papers and a cosine similarity-based recommendation system for suggesting similar papers to a given paper. The proposed approach is evaluated on a dataset of research papers, and the results show that it is effective in generating coherent and concise summaries and recommending relevant research papers. The findings of this study have important implications for researchers and practitioners in the field of natural language processing and information retrieval.