Datasets:
ArXiv:
License:
| { | |
| "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 | |
| } |