AI Literature Review Suite

David A. Tovar
7/27/2023
cs.DLcs.AIcs.HC

No official code found. Best implementation: datovar4/Ai_Literature_Review_Suite (21 stars, MIT, last commit 2025-03-27).

Abstract

The process of conducting literature reviews is often time-consuming and labor-intensive. To streamline this process, I present an AI Literature Review Suite that integrates several functionalities to provide a comprehensive literature review. This tool leverages the power of open access science, large language models (LLMs) and natural language processing to enable the searching, downloading, and organizing of PDF files, as well as extracting content from articles. Semantic search queries are used for data retrieval, while text embeddings and summarization using LLMs present succinct literature reviews. Interaction with PDFs is enhanced through a user-friendly graphical user interface (GUI). The suite also features integrated programs for bibliographic organization, interaction and query, and literature review summaries. This tool presents a robust solution to automate and optimize the process of literature review in academic and industrial research.

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Code Implementations(1)

datovar4/Ai_Literature_Review_SuiteCommunity100%
MIT
216PythonJul 27, 20231 years agoMIT

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Cite this paper

@article{tovar2023literature,
  title  = {AI Literature Review Suite},
  author = {David A. Tovar},
  year   = {2023},
  eprint = {2308.02443},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.02443v1}
}

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