| --- |
| language: |
| - pt |
| license: cc-by-nc-nd-4.0 |
| colorTo: blue |
| tags: |
| - text-summarization |
| - abstractive-summarization |
| - portuguese |
| - administrative-documents |
| - municipal-meetings |
| - bart |
| library_name: transformers |
| base_model: |
| - facebook/bart-base |
| --- |
| |
| # Bart-Base-Summarization-Council-PT: Abstractive Summarization of Portuguese Municipal Meeting Minutes Discussion Subjects |
|
|
| ## Model Description |
|
|
| **Bart-Base-Summarization-Council-PT** is an **abstractive text summarization model** based on **BART-base**, fine-tuned to produce concise and informative summaries of discussion subjects from **Portuguese municipal meeting minutes**. |
| The model was trained on a curated and annotated corpus of official municipal meeting minutes covering a variety of administrative and political topics at the municipal level. |
|
|
| **Try out the model**: [Hugging Face Space Demo](https://huggingface.co/spaces/anonymous12321/Citilink-Summ-PT) |
|
|
| ### Key Features |
|
|
| - 🧾 **Abstractive Summarization** – Generates natural, human-like summaries rather than extracts. |
| - 🇵🇹 **European Portuguese** – Optimized for official and administrative Portuguese. |
| - 🏛️ **Domain-Specific** – Trained on municipal meeting minutes and administrative discussions. |
| - ⚙️ **Fine-tuned BART** – Built upon `facebook/bart-base` using supervised fine-tuning. |
| - 🧠 **Fact-Aware Generation** – Produces short summaries that preserve factual content. |
|
|
| --- |
|
|
| ## Model Details |
|
|
| - **Architecture:** `facebook/bart-base` |
| - **Task:** Abstractive summarization (`text → summary`) |
| - **Framework:** 🤗 Transformers (PyTorch) |
| - **Tokenizer:** BART-base tokenizer (English vocabulary adapted for Portuguese text) |
| - **Max Input Length:** 1024 tokens |
| - **Max Summary Length:** 128 tokens |
| - **Training Objective:** Conditional generation (cross-entropy loss) |
| - **Dataset:** Portuguese municipal meeting minutes annotated with summaries |
|
|
| --- |
|
|
| ## How It Works |
|
|
| The model receives a discussion subject of a municipal meeting and outputs a short, coherent summary highlighting: |
| - The **main subject or topic** of discussion |
| - Any **decisions, motions, or proposals** made |
| - The **entities or departments** involved |
|
|
| ### Example Usage |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| |
| model_name = "anonymous12321/Bart-Base-Summarization-Council-PT" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
| |
| text = """ |
| 17. PROCESSO DE OBRAS N.º ***** -- EDIFIC\nPelo Senhor Presidente foi presente a esta reunião a informação n.º ****** da Secção de Urbanismo e Fiscalização -- Serviço de Obras Particulares que se anexa à presente ata. \nPonderado e analisado o assunto o Executivo Municipal deliberou por unanimidade aprovar as especialidades relativas ao processo de obras n.º ***** -- EDIFIC. |
| """ |
| |
| inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True) |
| summary_ids = model.generate(**inputs, max_length=128, num_beams=4, early_stopping=True) |
| print(tokenizer.decode(summary_ids[0], skip_special_tokens=True)) |
| |
| ``` |
|
|
| # 🧾 Model Output |
|
|
| **Output:** |
| > "O Executivo Municipal aprovou, por unanimidade, as especialidades relativas a um processo de obras particulares." |
|
|
| --- |
|
|
| ## 📊 Evaluation Results |
|
|
| ### Quantitative Metrics (on held-out test set) |
|
|
| | Metric | Score | Description | |
| |:-------|:------:|:------------| |
| | **ROUGE-1** | 0.556 | Unigram overlap between generated and reference summaries | |
| | **ROUGE-2** | 0.432 | Bigram overlap | |
| | **ROUGE-L** | 0.503 | Longest common subsequence overlap | |
| | **BERTScore (F1)** | 0.807 | Semantic similarity between summary and reference | |
|
|
| --- |
|
|
| ## ⚙️ Training Details |
|
|
| - **Pretrained Model:** `facebook/bart-base` |
| - **Optimizer:** AdamW (default in Hugging Face Trainer) |
| - **Learning Rate:** 2e-5 |
| - **Batch Size:** 4 |
| - **Epochs:** 3 |
| - **Scheduler:** Linear warmup |
| - **Loss Function:** Cross-entropy |
| - **Evaluation Metrics:** ROUGE (computed on validation set every 100 steps) |
| - **Evaluation Strategy:** Step-based evaluation (`eval_steps=100`) |
| - **Weight Decay:** 0.01 |
| - **Mixed Precision (fp16):** Enabled when CUDA is available |
|
|
| --- |
|
|
| ## 📚 Dataset Description |
|
|
| The model was trained on a specialized dataset of **Portuguese municipal meeting minutes**, consisting of: |
|
|
| - Discussion Subjects from official municipal meeting minutes. |
| - Decisions and deliberations across departments (urban planning, finance, education, etc.) |
| - Expert-annotated summaries per discussion segment |
|
|
| **Dataset sources include:** |
|
|
| - Six Portuguese municipalities meeting minutes |
|
|
| --- |
|
|
| ## ⚠️ Limitations |
|
|
| - **Language Restriction:** The model is optimized for Portuguese; performance may degrade in other languages. |
| - **Domain Dependence:** Best suited for administrative and institutional texts; less effective on informal or creative writing. |
| - **Length Sensitivity:** Very long transcripts (>1024 tokens) are truncated; chunking may be needed for full documents. |
| - **Generalization:** While robust within-domain, it may underperform on unseen domains or vocabulary. |
|
|
| --- |
|
|
| ## 📄 License |
|
|
| This model is released under the |
| **Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).** |
|
|
| --- |
|
|
