arxiv - summarization: open - source research paper abstract generation model, free abstract optimization for arXiv papers

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Arxiv Summarization

Developed by Talina06

A research paper summarization model fine-tuned from google/flan-t5-small, optimized for arXiv papers

Text Generation

Safetensors

EnglishOpen Source License:Apache-2.0 #Research paper abstracts #arXiv optimization #T5 fine-tuning

Downloads 56

Release Time : 3/12/2025

Model Overview

This model is designed to generate concise summaries from lengthy research papers, particularly suitable for academic paper summarization tasks

Model Features

Research Paper Optimization

Specifically optimized for arXiv research papers, better understanding academic terminology and paper structure

Efficient Summarization

Capable of extracting key information from lengthy research papers to generate concise summaries

Based on FLAN-T5

Built upon google/flan-t5-small, with strong text comprehension and generation capabilities

Model Capabilities

Research paper summarization

Academic text comprehension

Key information extraction

Use Cases

Academic Research

Automatic Paper Summarization

Automatically generates paper summaries for researchers, saving time

Produces concise and accurate paper summaries

Literature Review Assistance

Helps quickly understand large volumes of paper content

Improves literature reading efficiency

🚀 arxiv-summarization

This model is a fine - tuned version of google/flan - t5 - small, optimized for summarizing scientific abstracts.

🚀 Quick Start

This model is a fine-tuned version of google/flan-t5-small on a dataset of armanc/scientific_papers (arxiv). It is optimized for summarizing scientific abstracts.

✨ Features

Optimized for summarizing scientific abstracts.
Fine - tuned from google/flan - t5 - small on specific arxiv datasets.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import T5ForConditionalGeneration, T5Tokenizer

model = T5ForConditionalGeneration.from_pretrained("Talina06/arxiv-summarization")
tokenizer = T5Tokenizer.from_pretrained("Talina06/arxiv-summarization")

text = "Summarize: Deep learning is being used to advance medical research, particularly in cancer detection."
inputs = tokenizer(text, return_tensors="pt")
summary_ids = model.generate(**inputs)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

print("Generated Summary:", summary)

📚 Documentation

Model Details

Property	Details
Model Type	`google/flan-t5-small`
Training Data	Arxiv Research Papers (`article` → `abstract`)
Fine - Tuned Task	Text Summarization
Use Case	Generate shorter summaries of long research papers
License	Apache 2.0

Training Details

Training Data: 100k+ Arxiv research papers
Training Framework: Hugging Face Transformers
Hyperparameters:
- Learning Rate: 5e-5
- Batch Size: 8
- Epochs: 10
Hardware Used: TPU & GPU

Limitations

⚠️ Important Note

This model may struggle with very technical papers (e.g., complex math formulas).

Example Summaries

Original Abstract	Generated Summary
"Deep learning has transformed many fields... We propose a new CNN for cancer detection..."	"A CNN model is proposed for cancer detection using deep learning."
"Quantum computing has shown potential for cryptographic applications..."	"Quantum computing can be used in cryptography."

📄 License

This project is licensed under the Apache 2.0 license.

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