Automated News Summarization Using LLM Workflows

Authors: Danait Hishe, Alex Bekele
Date: 2026-02-28
Affiliation: Explore Ethiopia AI Research Lab

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Abstract

This study explores the use of large language models (LLMs) for automatic summarization of news articles. We implement a workflow where articles are processed, key sentences are extracted, and concise summaries are generated. Results show that our LLM-based workflow achieves an average ROUGE-1 score of 0.78, significantly outperforming baseline extractive summarizers.

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Introduction

With the exponential growth of online news, readers are overwhelmed with information. Efficient summarization tools can help distill essential content. Traditional extractive summarizers often fail to preserve context and readability. This project addresses this gap by leveraging LLMs to generate coherent, context-aware summaries of news articles.

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Methodology

We developed a modular LLM workflow for news summarization using the OpenAI GPT-4 API. The workflow consists of:

1. Article Preprocessing: Remove HTML tags, advertisements, and boilerplate text.
2. Key Sentence Extraction: Identify sentences containing main entities and events.
3. LLM Summarization: Feed extracted sentences into GPT-4 with a prompt template instructing it to generate a concise summary under 100 words.
4. Postprocessing: Correct formatting and ensure grammatical coherence.

Sample Code Snippet

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY")

def summarize_article(article_text):
    prompt = f"""
    You are a news editor. Summarize the following article in under 100 words:

    {article_text}
    """
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.3
    )
    return response.choices[0].message.content

# Example usage
article = "Ethiopia's tourism sector has seen a 20% increase in international visitors this year, driven by new travel initiatives and improved infrastructure."
summary = summarize_article(article)
print(summary)