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# Intro | |
<!-- - [x] objective/Aim of the practical part | |
- [x] tasks/ work packages, | |
- [x] Timeline and Milestones | |
- [x] Brief introduction of the practice partner | |
- [x] Description of theoretical part and explanation of how the content of the lecture(s)/seminar(s) supports student in completing the practical part. --> | |
## IDP Theme | |
IDP Theme: Developing a Literature Research Tool that Automatically Search Literature and Summarize the Research Trends. | |
## Objective | |
In this IDP, we are going to develop a literature research tool that enables three functionalities: | |
1. Automatically search the most recent literature filtered by keywords on three literature platforms: Elvsier, IEEE and Google Scholar | |
2. Automatically summarize the most popular research directions and trends in the searched literature from step 1 | |
3. visualize the results from step 1 and step 2 | |
## Timeline & Milestones & Tasks | |
 | |
#### Tasks | |
| Label | Start | End | Duration | Description | | |
| ------- |------------| ---------- |----------| -------------------------------------------------------------------------------------------------------- | | |
| Task #1 | 15/11/2022 | 15/12/2022 | 30 days | Implement literature search by keywords on three literature platforms: Elvsier, IEEE, and Google Scholar | | |
| Task #2 | 15/12/2022 | 15/02/2023 | 60 days | Implement automatic summarization of research trends in the searched literature | | |
| Task #3 | 15/02/2022 | 15/03/2022 | 30 days | visualization of the tool (web app) | | |
| Task #4 | 01/03/2022 | 01/05/2022 | 60 days | write report and presentation | | |
## Correlation between the theoretical course and practical project | |
The accompanying theory courses *Machine Learning and Optimization* or *Machine Learning for Communication* teach basic and advanced machine learning (ML) and deep learning (DL) knowledge. | |
The core part of the project, in my opinion, is the automatic summarization of research trends/directions of the papers, which can be modeled as a **Topic Modeling** task in Natural Language Processing (NLP). This task requires machine learning and deep learning knowledge, such as word embeddings, transformers architecture, etc. | |
Therefore, I would like to take the Machine Learning and Optimization course or Machine learning for Communication course from EI department. And I think these theory courses should be necessary for a good ML/DL basis. | |