A software project aimed at enhancing the process of academic literature search and analysis through automation and AI assistance. The core idea of the project is to leverage modern APIs and machine learning models to simplify the tasks of searching, analyzing, and managing academic literature, thus enhancing the efficiency of academic research and literature review processes.
The primary goal of this project is to create an application that helps users search for, analyze, and manage academic articles from the arXiv database in the fields of mathematics, physics, astronomy, computer science, quantitative biology, statistics, and quantitative finance.
The script addresses the problem of efficiently locating relevant academic papers, determining their relevance and similarity, and organizing the search results. Users often face challenges in navigating vast amounts of academic literature, understanding the connections between different works, and managing their research findings.
API Requests: The script uses the requests library to fetch data from the arXiv API, which allows users to query for articles based on specific keywords. XML Parsing: It parses the XML response from the arXiv API to extract useful information like titles, summaries, and URLs of the articles. Natural Language Processing (NLP): Through the OpenAI API, it employs a model to compute the semantic similarity between article titles to detect if an article has already been processed or is similar to previous entries. Regular Expressions (Regex): Used for data cleaning and manipulation, particularly in normalizing titles. File Management: The script organizes search results into CSV files within dynamically created directories for easy access and reference. Streamlit: For user interface, Streamlit is used to create an interactive web app where users can input search queries and view results.
The script is particularly useful for researchers, academicians, and students who are engaged in extensive literature reviews and need an efficient tool to streamline their search and analysis process. It helps users save time, manage their research literature, and draw insights from the vast availability of academic articles.
Automation of repetitive tasks: Automates the search and initial analysis of academic papers, saving valuable time and effort. Organization of information: Helps in organizing search results systematically, making it easier to retrieve and refer to needed information. Enhanced accessibility: Through a user-friendly interface provided by Streamlit, users with minimal technical skills can also access and use this tool effectively.