the readme file is written in Markdown code here is a Markdown cheatsheet
Summarizing a stack of papers involves systematically reviewing and condensing the key information from each paper to provide a concise overview. It is time-consuming. To save readers’ time, it might be helpful to utilize LLMs. Leveraging LLMs like GPT-3 for summarizing a stack of papers involves using natural language processing capabilities to generate concise and coherent summaries. It might be feasible to have two stages to come out the summarization of a stack of papers. The first step is to generate a summary for each paper using LLMs, and the second step is to aggregate all summaries of the stack of papers into a cohesive document.
The website is structured to provide easy access to essential details about each team member, fostering a sense of unity and camaraderie. It serves as a digital directory, offering insights into the unique skills and contributions of each individual, thereby promoting transparency and synergy within our team. One of the key features of our website is its extensive archive of meetings. This includes both past and future engagements, ensuring that every team member is kept abreast of our progress and plans. The archive is a treasure trove of knowledge, capturing the essence of each meeting, the decisions made, the challenges encountered, and the solutions proposed. It also provides a roadmap of upcoming meetings, helping us stay organized and prepared. The website is a testament to our technical prowess, having been crafted using HTML, CSS, and JavaScript. These technologies were chosen for their versatility and wide acceptance in the web development community. Our team has leveraged these languages to create a user-friendly interface and a robust backend, ensuring a smooth and engaging user experience. Our final product, the culmination of our team’s hard work and dedication, will be proudly hosted on this website. This will not only serve as a demonstration of our capabilities but also provide a platform for users to interact with our product. We believe that this will greatly enhance the visibility and accessibility of our product, paving the way for feedback and improvements. In essence, our website is more than just a platform; it’s a dynamic space that reflects our team’s spirit, showcases our product, and fosters a culture of openness and collaboration. It is a testament to our journey, our growth, and our unwavering commitment to delivering a product that we are truly proud of.
Link To Powerpoint Presantation
https://www.kaggle.com/datasets?search=documents Kaggle, a Google subsidiary, is a virtual gathering place for data scientists and machine learning practitioners. It provides a platform where users can discover datasets for AI model development, share datasets, collaborate with other data enthusiasts, and participate in contests to address data science problems. Since its inception in 2010, Kaggle has been hosting machine learning and data science competitions, and it also provides a public data platform and cloud-based workspace for data science and AI learning.
https://paperswithcode.com/datasets?task=text-summarization&page=1 Papers with Code is a community-driven platform that provides a comprehensive resource for Machine Learning research, including papers, code, datasets, and evaluation methods. The platform encourages open collaboration, facilitated by NLP and ML technologies. All content is freely available under the CC-BY-SA license, similar to Wikipedia, and contributions from users are welcomed. In addition to its main focus, the platform also hosts specialized sections for fields like astronomy, physics, computer sciences, mathematics, and statistics.
Will be organizing meetings, keeping track of progress, will be developing and testing models to improve text summarization, while contributing to the overall project in code, suggestions, and documentation.
will be developing and testing models to improve text summarization, while contributing to the overall project in code, suggestions, and documentation.
will be working on both testing models and hosting the results, while contributing to the overall project in code, suggestions, and documentation.
will be working on both testing models and hosting the results, while contributing to the overall project in code, suggestions, and documentation.
will be working on both testing models and hosting the results, while contributing to the overall project in code, suggestions, and documentation.
will be working on both testing models and hosting the results, while contributing to the overall project in code, suggestions, and documentation. overall the roles above are not limited but are more flexible based on experience and collaboration
| Dates | Description |
|---|---|
| T 01/23/2024 | - Meeting Group members - Discussing the project - Assigned roles - Layed out a plan |
| R 01/25/2024 | - Discussing stage 1 - Progress on website - presanting pontential models we will use in our project |
| T 01/30/2024 | - worked on slides and report for the presantation |
| R 02/01/2024 | - began discussing Phase 2 |
| T 02/06/2024 | - had an extensive discussion on available models (openAI vs T5) |
| F 02/09/2024 | - officially chose T5 as our model and began discussing testing |