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Scientific Paper Summarizer and Research Assist

This website was designed to help researchers and students help simplify understanding and working with scientific articles and research papers.This tool currently only wokrs for Springr non-mathematical papers only.New versions will aim to add more papers and capabilities, all contributions are openly welcomed, in building a student and research friendly application.

Features:

• Automated Text Mining: Utilizes TF-IDF algorithm to extract relevant text segments from scientific papers.
• Content Generation: Employs BART (Bidirectional and Auto-Regressive Transformers) model for generating comprehensive summaries from mined text segments.
• PowerPoint Presentation: Integrates with the Python pptx library to create visually appealing PowerPoint slides containing the summarized content.
• Text-to-Speech: Incorporates the Realistic Text to Speech API by VidLab for converting text summaries into natural-sounding speech, enhancing accessibility and user experience.

Usage:

Setup:

• Clone the repository and install the required dependencies.

pip install -r requirements.txt

• To start python backend api

python app.py

• To run React website

cd Frontend

npm start

Pre-trained Models:

• Download pre-trained BART models and configure the transformers library accordingly.

Summarization:

• Run the summarization script, by providing the link through the React website.

Output:

• View the generated Summary , PowerPoint presentation (summary.pptx) containing the summarized content as well as audio file (audio.mp3).

The Outputs folder will contain all the non-text outputs

Contribution:

• Jafar N
• Sharon T Saju
• Sreedev TS , xreedev@gmail.com

Module Design

app.py : Flask api to treat requests ppt.py : create ppt based on template.pptx BART.py : Module responsible for summarization using BART data.py :Used to store any data repetitively used dataProcesser.py : Used to remove stop words main.py:To test all backend features pdfextract.py: Used to extract text from pdfs(incomplete) TFIDF.py:Extractive summarization using TF-IDF algorithm tts.py : Test to speech module webcrawler.py:Webcrawl and parse data from springr articles

License:

This project is licensed under the MIT License. See the LICENSE file for details.

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