A Complete Guide for Creating an AI Assistant for Summarizing YouTube Videos — Part 2

A Complete Guide for Creating an AI Assistant for Summarizing YouTube Videos — Part 2.

Overview

Author(s): Amin Kamali

Originally published on Towards AI.

Summarize a video transcript using LangChain and the Falcon model efficiently using Quantization
Image generated by the author with Playground.ai

In the previous part of this series, we captured the transcript of a YouTube video. In this post, we will take that transcript and create a summarization pipeline that distills the text into a concise summary including key points and arguments presented in the video (see Figure 1). To this end, we will use LangChain to create the summarization pipeline and HuggingFace to make inferences using open-source LLMs. You may want to have a look at a demo of the tool or the code hosted on Hugging Face Spaces. Feel free to give it… Read the full blog for free on Medium.

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Published via Towards AI

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AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.