The entire code for the project is there in the file literature.py

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Automating Literature Review Summarization Using LangChain and Generative AI

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Author: Bhargav B J

Abstract

Conducting literature reviews is a fundamental aspect of academic research, yet it remains a time-consuming and labor-intensive process. This paper presents an automated pipeline that leverages LangChain and Google's Generative AI to streamline the literature review process. By integrating the Papers with Code API for paper retrieval, PyMuPDF for PDF text extraction, and the Gemini 2.0 Flash model for summarization, the system efficiently generates concise summaries of research papers. This approach aims to reduce the manual effort involved in literature reviews, enabling researchers to focus more on analysis and synthesis.

1. Introduction

The exponential growth of scientific publications has made it increasingly challenging for researchers to stay abreast of developments in their fields. Traditional methods of conducting literature reviews are not only time-consuming but also prone to oversight due to the sheer volume of available literature. Automating this process can significantly enhance research efficiency and accuracy.

Recent advancements in Large Language Models (LLMs) and frameworks like LangChain have opened new avenues for automating various aspects of research, including literature reviews. This paper introduces a system that combines these technologies to automate the retrieval and summarization of research papers, thereby facilitating a more efficient literature review process.

2. Related Work

Several tools have been developed to assist in literature reviews. For instance, LitLLM is a toolkit that employs Retrieval-Augmented Generation (RAG) principles to generate related work sections by retrieving and summarizing relevant papers based on user-provided abstracts . Similarly, LatteReview utilizes a multi-agent framework to automate systematic reviews, incorporating modular agents for tasks like screening and data extraction .

While these tools offer valuable functionalities, they often require complex setups or are tailored for specific domains. The system presented in this paper aims for simplicity and general applicability, making it accessible to a broader range of researchers.

3. Methodology

The proposed system comprises the following components:

3.1. Paper Retrieval

Utilizing the Papers with Code (PWC) API, the system searches for research papers based on user-defined queries. This API provides access to a vast repository of machine learning papers, ensuring relevant and up-to-date literature is retrieved.

3.2. PDF Text Extraction

Once the relevant papers are identified, their PDFs are downloaded. The PyMuPDF library (imported as fitz) is employed to extract text from these PDFs, ensuring that the content is accurately captured for summarization.

3.3. Summarization

The extracted text is then processed using Google's Generative AI, specifically the Gemini 2.0 Flash model. This model generates concise summaries by identifying and extracting key points from each section of the papers.

3.4. Integration with LangChain

LangChain serves as the framework that orchestrates the entire process. It facilitates seamless integration between the different components, ensuring that each step—from retrieval to summarization—is executed efficiently.

4. Implementation Details

The system is implemented in Python and requires the following libraries:

• requests for API interactions
• pymupdf for PDF text extraction
• python-dotenv for managing environment variables
• langchain-google-genai for integrating Google's Generative AI

Users must provide their Google API Key and PWC API Key, stored securely in a .env file. The system is designed to be user-friendly, with clear instructions provided in the repository's README file.

5. Results

The system effectively automates the literature review process, generating concise summaries that capture the essence of each paper. This automation significantly reduces the time and effort required for literature reviews, allowing researchers to allocate more resources to analysis and interpretation.

6. Discussion

While the system demonstrates promising results, there are areas for improvement. For instance, integrating more advanced retrieval techniques or expanding the scope beyond machine learning papers could enhance its utility. Additionally, incorporating user feedback mechanisms could further refine the summarization process.

7. Conclusion

This paper presents a streamlined approach to automating literature reviews by integrating LangChain with Google's Generative AI. The system simplifies the process of retrieving and summarizing research papers, offering a valuable tool for researchers across various domains.

References

• Agarwal, S., Laradji, I. H., Charlin, L., & Pal, C. (2024). LitLLM: A Toolkit for Scientific Literature Review. arXiv preprint arXiv:2402.01788.

• Rouzrokh, P., & Shariatnia, M. (2025). LatteReview: A Multi-Agent Framework for Systematic Review Automation Using Large Language Models. arXiv preprint arXiv:2501.05468.