paper-finder

Multi-database paper search across arXiv and Semantic Scholar uses custom keywords to find and rank scholarly papers. It applies de-duplication and scoring across relevance, recency, popularity, and quality to return top results in structured JSON format.

Yes, arXiv and Semantic Scholar paper search supports optional filters including top-n, categories, and min-year. These filters narrow results to fit specific research tasks before scoring and ranking the papers.

Ranking academic papers for literature reviews uses a multi-factor scoring system evaluating relevance, recency, popularity, and quality. It searches across arXiv and Semantic Scholar, de-duplicates the results, and outputs the top papers as structured notes.

Yes, this multi-database paper search outputs top papers in a structured JSON format specifically designed for downstream note generation. It de-duplicates and scores results from arXiv and Semantic Scholar before generating the output.

Querying both arXiv and Semantic Scholar includes automatic de-duplication of search results. It identifies overlapping scholarly papers across the multiple databases before applying scoring and ranking to the final list.

Yes, running this academic paper search tool requires the Python requests and pyyaml dependencies. These libraries handle the multi-database queries to arXiv and Semantic Scholar and process the configuration needed for search filters and scoring.