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{
"name": "40_Text_Summarization_BART_CNNDailyMail_DL",
"query": "Develop a system that performs text summarization system using the BART model with the CNN/Daily Mail dataset. Start by loading and preparing the dataset in `src/data_loader.py`, then perform data preprocessing such as removing HTML tags and punctuation in `src/data_loader.py`. Import a pre-trained BART model for text summarization in `src/model.py` to generate summaries. Save the generated summaries to `results/summaries.txt`. Visualize the length distribution of these summaries using seaborn and save the visualization to `results/figures/summary_length_distribution.png`. Additionally, implement an interactive Streamlit web page in `src/visualize.py`, which allows users to view input texts and their generated summaries. Finally, generate a report covering data preprocessing and generation results, and save it as `results/text_summarization_report.pdf`.",
"tags": [
"Generative Models",
"Natural Language Processing"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"CNN/Daily Mail\" news dataset is used, including loading and preparing the dataset in `src/data_loader.py`.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
0
],
"criteria": "Data preprocessing is performed in `src/data_loader.py`, including removing HTML tags and punctuation.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [],
"criteria": "A pre-trained \"BART\" model is imported for text summarization in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [
1,
2
],
"criteria": "The generated summary results are saved in `results/summary_results.txt`.",
"category": "Other",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
3
],
"criteria": "The length distribution of the generated summaries is visualized using \"seaborn,\" and the plot is saved as `results/figures/summary_length_distribution.png`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
3
],
"criteria": "An interactive web page is created using \"Streamlit\" to display input texts and their generated summaries and implemented in `src/visualize.py`.",
"category": "Human Computer Interaction",
"satisfied": null
},
{
"requirement_id": 6,
"prerequisites": [
3
],
"criteria": "A report covering data preprocessing, model training, and generation results is generated and saved as `results/text_summarization_report.pdf`.",
"category": "Other",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The interactive \"Streamlit\" webpage should allow users to input new text and generate summaries in real-time.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The report should include a discussion on how different hyperparameter settings affected the model's performance.",
"satisfied": null
},
{
"preference_id": 2,
"criteria": "During development, the \"Streamlit\" application should be efficiently managed to avoid unnecessary resource usage.",
"satisfied": null
}
],
"is_kaggle_api_needed": false,
"is_training_needed": false,
"is_web_navigation_needed": false
} |