Generating Summarized Restaurant Reviews using Language Models

[limwualice]

Description: Use Language Models (LLMs) to generate summarized reviews for each restaurant in a dataset. The goal is to leverage the power of LLMs to automatically generate concise and informative summaries that capture the essence of customer reviews.

Key Points to Address:

• Dataset and Preprocessing: Clean the dataset and preprocess it in a way that will work for the llm
• LLM Training: Outline the process of training a language model using the restaurant review dataset. Discuss the choice of LLM architecture (e.g., GPT, BERT) and any fine-tuning techniques that may be employed.
• Review Summarization Approach: Use techniques such as extractive summarization or abstractive summarization and apply to restaurant reviews.
• Evaluation Metrics: Define the metrics that will be used to evaluate the quality and effectiveness of the generated summaries. Consider metrics such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation) or BLEU (Bilingual Evaluation Understudy) scores.