metadata
title: Lit2Vec TL;DR Summarizer
emoji: 🧪
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.42.0
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_scopes:
- inference-api
short_description: Abstractive TL;DR summarizer for chemistry research abstracts.
Lit2Vec TL;DR is an abstractive summarization tool for chemistry research abstracts, built using Gradio and a fine-tuned DistilBART model. It generates concise, structured summaries capturing the methods, results, and significance of scientific papers.
This app uses models hosted on the 🤗 Hugging Face Hub and supports reproducible, legally compliant summarization data. Ideal for knowledge graph construction, semantic indexing, and literature triage in chemical sciences.
- 🔬 Model:
Bocklitz-Lab/lit2vec-tldr-bart-model - 📁 Dataset:
lit2vec-tldr-bart-dataset - 💻 Code: GitHub Repo
Paste any chemistry abstract to get a TL;DR-style structured summary with just one click.