t5-large-finetuned-xsum-cnn

T5-Large Fine-tuned on the combined XSum + CNN/DailyMail Datasets

Task: Abstractive Summarization (English)
Base Model: google-t5/t5-large
License: MIT

Overview

This model is a T5-Large checkpoint fine-tuned jointly on XSum and CNN/DailyMail datasets. It produces concise, abstractive summaries and has been widely adopted as a baseline in summarization research.

Performance ~ On XSum test set

Usage

Quick Start

from transformers import pipeline

summarizer = pipeline("summarization", model="sysresearch101/t5-large-finetuned-xsum-cnn")

article = "Your article text here..."
summary = summarizer(article, max_length=80, min_length=20, do_sample=False)
print(summary[0]['summary_text'])

Advanced Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("sysresearch101/t5-large-finetuned-xsum-cnn")
model = AutoModelForSeq2SeqLM.from_pretrained("sysresearch101/t5-large-finetuned-xsum-cnn")

inputs = tokenizer("summarize: " + article, return_tensors="pt", max_length=512, truncation=True)
outputs = model.generate(
    **inputs,
    max_length=80,
    min_length=20,
    num_beams=4,
    no_repeat_ngram_size=2,
    length_penalty=1.0,
    repetition_penalty=2.5,
    use_cache=True,
    early_stopping=True,
    do_sample = True,
    temperature = 0.8,
    top_k = 50,
    top_p = 0.95
)

summary = tokenizer.decode(outputs[0], skip_special_tokens=True)

Training Data

• XSum: BBC articles with single-sentence summaries
• CNN/DailyMail: News articles with multi-sentence summaries

Intended Use

• Primary: Summarization.
• Secondary: Educational demonstrations, reproducible baselines, Research benchmarking, academic studies on summarization

Limitations

• Optimized for English news text; performance may vary on other domains
• Tends to produce very concise summaries (18-20 tokens average)
• No built-in fact-checking or content filtering

Citation

@misc{stept2023_t5_large_xsum_cnn_summarization,
  author = {Shlomo Stept (sysresearch101)},
  title = {T5-Large Fine-tuned on XSum + CNN/DailyMail for Abstractive Summarization},
  year = {2023},
  publisher = {Hugging Face},
  url = {https://huggingface.co/sysresearch101/t5-large-finetuned-xsum-cnn}
}

Papers Using This Model

• Zhu et al. (2023). Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization. ACL 2023 (Long).
• European Food Safety Authority. (2023). Implementing AI Vertical use cases – Scenario 1. EFSA Journal, Special Publication EN-8223. https://doi.org/10.2903/sp.efsa.2023.EN-8223
• (Forthcoming) Budget-Constrained Learning to Defer for Autoregressive Generation (under review, ICLR 2025)

Contact

Created by Shlomo Stept (ORCID: 0009-0009-3185-589X) DARMIS AI

• Website: shlomostept.com
• LinkedIn: linkedin.com/in/shlomo-stept