bangla_resume_summarizerMT5

Bangla Resume Summarizer — mT5

Fine-tuned mT5_multilingual_XLSum for abstractive summarization of Bangla-language resumes, section by section. Developed as part of the Onneshon thesis project at Bangladesh University of Professionals (BUP).

Given the raw sentences of a resume section (Objective, Experience, Education, or Skill), the model generates a fluent 2–3 sentence Bangla summary with strong semantic alignment to human-verified references.

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Model Details

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Dataset

Trained on Onneshon — an original Bangla resume dataset of 100 annotated resumes spanning 20+ professions.
Published on Mendeley Data: DOI: 10.17632/4md7bx6fd7.1

Reference summaries were generated using GPT-OSS-120B (via OpenRouter) and human-verified, producing 396 section-level summary pairs across 4 categories: Objective, Experience, Education, Skill.

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Evaluation Results

Evaluated against human-verified abstractive reference summaries using a Bengali-aware ROUGE tokenizer and semantic similarity:

Note on ROUGE scores: ROUGE measures exact word overlap. Since references are abstractive paraphrases and Bangla has rich morphology, ROUGE can still underestimate quality relative to semantic similarity. Semantic similarity of 0.818 — the highest among all 4 methods evaluated — confirms this model produces summaries most aligned in meaning with human-verified references.

Per-section ROUGE-1 and Semantic Similarity:

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Comparison: BanglaT5 vs mT5

mT5 produces more fluent, semantically natural summaries (higher semantic sim overall). BanglaT5 has higher word-level fidelity (higher ROUGE) for factual sections. Use mT5 when fluency and meaning alignment are priorities.

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Usage

⚠️ Important: This model requires the Bengali language token <2bn> prepended to every input. Without it, the model defaults to non-Bengali output.

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

LANG_TOKEN = "<2bn>"

tokenizer = AutoTokenizer.from_pretrained("your-username/bangla-resume-summarizer-mt5", use_fast=False)
model     = AutoModelForSeq2SeqLM.from_pretrained("your-username/bangla-resume-summarizer-mt5")
model.eval()

def summarize(text):
    # Prepend Bengali language token — required for this model
    input_text = LANG_TOKEN + " " + text.strip()
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512)
    outputs = model.generate(
        inputs["input_ids"],
        max_length=128,
        min_length=20,
        num_beams=4,
        length_penalty=1.2,
        repetition_penalty=2.0,
        no_repeat_ngram_size=3,
        early_stopping=True
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example — Objective section
text = "অভিজ্ঞতা সম্পন্ন সফ্টওয়্যার ইঞ্জিনিয়ার হিসেবে একটি ডায়নামিক টিমে যোগদান করা যেখানে আমার জাভা এবং স্প্রিং ফ্রেমওয়ার্কের দক্ষতা কাজে লাগিয়ে প্রতিষ্ঠানের সাফল্যে অবদান রাখতে পারি"
print(summarize(text))

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Input Format

Feed one section at a time. Join multiple sentences with । (Bengali danda). The <2bn> token is added automatically in the usage example above:

<2bn> sentence_1 । sentence_2 । sentence_3

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Limitations

• Trained on only 317 pairs — a small dataset by deep learning standards. Outputs may be generic for unusual professions.
• Optimized for resume text. Performance on other Bangla document types is untested.
• Not suitable for very long inputs (>512 tokens); truncation will occur.
• The <2bn> language token is mandatory — omitting it will cause incorrect (non-Bengali) output.
• Compared to BanglaT5, this model has lower ROUGE on factual sections (Experience, Skill) but higher semantic similarity overall.

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Citation

If you use this model, please cite the Onneshon dataset and XL-Sum:

@misc{onneshon2026,
  title     = {Onneshon: A Bangla Resume NLP Dataset},
  author    = {Tanvir and Shruti Khisa and Shaira Akther Diba and Fazli Rabbi Noor},
  year      = {2026},
  doi       = {10.17632/4md7bx6fd7.1},
  publisher = {Mendeley Data}
}

@inproceedings{hasan-etal-2021-xl,
  title     = {XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages},
  author    = {Hasan, Tahmid and Bhattacharjee, Abhik and Islam, Md. Saiful and Mubasshir, Kazi and Li, Yuan-Fang and Kang, Yong-Bin and Rahman, M. Sohel and Shahriyar, Rifat},
  booktitle = {Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021},
  year      = {2021},
  pages     = {4693--4703}
}

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Project

Part of the Onneshon thesis project — a Bangla NLP pipeline for resume processing.

• Dataset: Mendeley Data DOI: 10.17632/4md7bx6fd7.1
• Institution: Bangladesh University of Professionals (BUP), Dhaka, Bangladesh
• Supervisor: Rumana Yasmin, Lecturer, CSE Department, BUP