LLMs tested for automated financial news summarization

Researchers at George Washington University explored the use of Large Language Models (LLMs) for automating financial news summarization in Fall 2023. They developed a pipeline integrating news articles, company information, and stock data, converting numerical stock data into natural language narratives. The study tested various LLMs, including Falcon-7B-Instruct and BART-Large-XSum, with summarization techniques like Summarize Chains and Retrieval-Augmented Generation (RAG). Falcon-7B with Summarize Chains yielded the best results, accurately and coherently summarizing news events, while RAG approaches showed issues with repetition and hallucination in smaller models. AI

RANK_REASON Academic paper detailing empirical study of LLMs for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• AAPL
• BART-Large-XSum
• DistilBART-CNN-12-6
• FAISS
• Falcon-7B-Instruct
• George Washington University
• GOOGL
• GPT (text-davinci-003)
• Large Language Models
• META
• News API
• NVDA
• Wikipedia
• Yahoo Finance

• paper
• product

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →