dialogue-summarizer

📝 Dialogue Summarizer using FLAN-T5

This model is a fine-tuned version of google/flan-t5-base (replace with flan-t5-small if that's what you used) for the task of dialogue summarization. It generates concise summaries from conversational dialogues while preserving the key information.

🚀 Model Details

• Base Model: google/flan-t5-base
• Task: Dialogue Summarization
• Framework: PyTorch
• Library: Hugging Face Transformers
• Fine-tuning Dataset: SAMSum Dialogue Summarization Dataset

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📊 Dataset

The model was fine-tuned on the SAMSum Dataset, which contains messenger-style conversations paired with human-written summaries.

Example:

Input Dialogue

Amanda: Are we still meeting tomorrow?
Jerry: Yes, let's meet at 10 AM.
Amanda: Perfect. See you then!

Generated Summary

Amanda and Jerry confirm their meeting for tomorrow at 10 AM.

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🧠 Training Details

• Epochs: 10
• Batch Size: 8
• Weight Decay: 0.01
• Warmup Steps: 500
• Optimizer: AdamW (Transformers default)
• Maximum Input Length: 512 tokens
• Maximum Summary Length: 150 tokens

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💻 Usage

Install dependencies:

pip install transformers torch

Load the model:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "Ayush5605/dialogue-summarizer"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

Generate a summary:

dialogue = """
Amanda: Are we still meeting tomorrow?
Jerry: Yes, let's meet at 10 AM.
Amanda: Perfect. See you then!
"""

inputs = tokenizer(
    dialogue,
    return_tensors="pt",
    max_length=512,
    truncation=True
)

summary_ids = model.generate(
    **inputs,
    max_length=150,
    num_beams=4,
    early_stopping=True
)

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

print(summary)

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📈 Intended Use

This model is intended for:

• Dialogue summarization
• Chat transcript summarization
• Meeting summarization
• Customer support conversation summarization
• Educational and research purposes

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⚠️ Limitations

• Performance depends on the quality and style of the input dialogue.
• Very long conversations (>512 tokens) are truncated.
• The model may omit minor conversational details while generating concise summaries.
• It has been fine-tuned primarily on English dialogue data.

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📚 Training Framework

• Python
• PyTorch
• Hugging Face Transformers
• Hugging Face Datasets

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👨‍💻 Author

Ayush Auti

• GitHub: https://github.com/Ayush5605
• Hugging Face: https://huggingface.co/Ayush5605

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📄 Citation

If you use this model in your work, please cite the original FLAN-T5 paper and the SAMSum dataset.

@article{flan2022,
  title={Scaling Instruction-Finetuned Language Models},
  author={Chung, Hyung Won and others},
  year={2022}
}

@inproceedings{samsum,
  title={SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization},
  author={Gliwa et al.},
  year={2019}
}