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doc fix: Update README.md to remove specific dicscription of paragraph-1 (#621)

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* Update README.md

According to #https://github.com/opea-project/GenAIExamples/issues/338: 

The motivation paragraph 2 is more general and perhaps should move up as paragraph-1. Original paragraph-1 gets too specific into legal documents prematurely.

so Remove the paragraph 1 as it is not related.

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Ying Hu
2024-08-19 11:44:41 +08:00
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@@ -1,8 +1,6 @@
# Document Summarization Application
In a world where data, information, and legal complexities are prevalent, the volume of legal documents is growing rapidly. Law firms, legal professionals, and businesses are dealing with an ever-increasing number of legal texts, including contracts, court rulings, statutes, and regulations. These documents contain important insights, but understanding them can be overwhelming. This is where the demand for legal document summarization comes in.
Large Language Models (LLMs) have revolutionized the way we interact with text. These models can be used to create summaries of news articles, research papers, technical documents, and other types of text. Suppose you have a set of documents (PDFs, Notion pages, customer questions, etc.) and you want to summarize the content. In this example use case, we utilize LangChain to implement summarization strategies and facilitate LLM inference using Text Generation Inference on Intel Xeon and Gaudi2 processors.
Large Language Models (LLMs) have revolutionized the way we interact with text. These models can be used to create summaries of news articles, research papers, technical documents, legal documents and other types of text. Suppose you have a set of documents (PDFs, Notion pages, customer questions, etc.) and you want to summarize the content. In this example use case, we utilize LangChain to implement summarization strategies and facilitate LLM inference using Text Generation Inference on Intel Xeon and Gaudi2 processors.
The architecture for document summarization will be illustrated/described below: