Introduction
In a world that thrives on instant updates and real-time information, news delivery systems must keep pace with growing demands. The AI News Automation project is a . NET-based solution designed to automate the process of discovering, summarizing, and publishing trending news articles. It leverages modern software practices, integrates artificial intelligence, and demonstrates how automation can enhance productivity and content quality.
Built with C# and powered by .NET 8, this system efficiently processes RSS feeds, fetches HTML content, summarizes it using AI, generates contextual images, and publishes well-structured articles. The solution encapsulates the end-to-end automation of news curation, providing a foundation for scalable, AI-driven publishing workflows. In this article, we examine its components, enriched with real-world code snippets and engineering insights.
The goal is to provide a comprehensive understanding of how to build similar systems or enhance existing pipelines using proven design principles, service abstraction, and AI integrations. Each section below is crafted to reflect professional use cases and development standards.
1. Extracting and Parsing Content with HtmlFetcher
The HtmlFetcher class is designed to perform dynamic content extraction by making search engine queries and parsing their results. The class uses the HtmlAgilityPack to load and manipulate HTML documents. It cycles through a list of User-Agent strings to mimic real browser traffic and avoid detection from anti-bot systems.
public async Task<string> FetchHtmlContentAsync(string query)
{
var random = new Random();
var selectedUserAgent = UserAgents[random.Next(UserAgents.Length)];
var url = new UriBuilder("https://duckduckgo.com/html/")
{
Query = $"q={Uri.EscapeDataString(query)}"
}.ToString();
_client.DefaultRequestHeaders.Clear();
_client.DefaultRequestHeaders.Add("User-Agent", selectedUserAgent);
var response = await _client.GetAsync(url);
response.EnsureSuccessStatusCode();
return await response.Content.ReadAsStringAsync();
}
This method not only performs HTTP GET requests but is also equipped with logging and exception handling to monitor response times and catch errors such as timeouts. After fetching, the content is parsed to extract links and article summaries.
public static string ExtractResults(string htmlContent)
{
var htmlDoc = new HtmlDocument();
htmlDoc.LoadHtml(htmlContent);
var resultsDiv = htmlDoc.DocumentNode.SelectSingleNode("//div[@id='links']");
return resultsDiv?.InnerHtml ?? "<div>No results found.</div>";
}
2. Structuring Data with Models: NewsArticle and PublishedArticle
Models provide the structure for both intermediate and final representations of content. The NewsArticle class captures raw inputs parsed from RSS feeds, while the PublishedArticle class represents fully processed and publishable content.
public class NewsArticle
{
public string Title { get; set; } = string.Empty;
public string Content { get; set; } = string.Empty;
}
The PublishedArticle includes additional metadata like summaries, image URLs, and publication timestamps. This ensures the final output is well-formed and CMS-compatible.
public class PublishedArticle
{
public string Title { get; set; } = string.Empty;
public string Summary { get; set; } = string.Empty;
public string ImageUrl { get; set; } = string.Empty;
public DateTime PublishedDate { get; set; }
}
This design encourages separation of concerns—keeping parsing logic independent from publishing rules. It also enables easier testing, serialization, and eventual API exposure if needed.
3. Aggregating News and Generating Content with NewsService
The NewsService class handles news aggregation by reading from multiple RSS feeds. It parses each feed into a list of NewsArticle objects. Additionally, it includes AI-driven methods to generate article summaries and image URLs.
public async Task<List<NewsArticle>> GetTrendingTechNewsAsync()
{
var articles = new List<NewsArticle>();
foreach (var feedUrl in rssFeeds)
{
using var reader = XmlReader.Create(feedUrl);
var feed = SyndicationFeed.Load(reader);
foreach (var item in feed.Items)
{
articles.Add(new NewsArticle
{
Title = item.Title.Text,
Content = item.Summary?.Text ?? string.Empty
});
}
}
return articles;
}
For content enrichment, NewsService simulates integration with external AI services.
public async Task<string> GenerateSummaryAsync(string content)
{
await Task.Delay(1000);
return content.Length > 250
? content.Substring(0, 250) + "..."
: content;
}
4. Publishing Articles via CMSService
The CMSService class manages publication. For the purpose of this prototype, it stores content in memory and logs it to the console, simulating a post to a CMS.
public void PublishContent(PublishedArticle article)
{
_publishedArticles.Add(article);
Console.WriteLine($"Published: {article.Title}");
Console.WriteLine($"{article.Summary}");
}
// The class also provides retrieval capability for UI or API exposure
public List<PublishedArticle> GetPublishedContent()
{
return _publishedArticles;
}
While this implementation is simplified, it provides an ideal interface to be replaced with real CMS integrations (e.g., Contentful, WordPress, or custom APIs). This architecture ensures that publishing logic remains encapsulated and easy to adapt.
5. Orchestrating the Workflow with SchedulerService
Automation is orchestrated by SchedulerService, which runs at scheduled intervals to execute the entire content pipeline. The service uses a timer to trigger periodic executions of the article generation and publishing routine.
public void StartScheduler()
{
_timer = new Timer(
async _ => await GenerateAndPublishNewsAsyncOrg(),
null,
TimeSpan.Zero,
TimeSpan.FromHours(4)
);
}
Inside the scheduled task, AI-generated summaries and images enhance the news content before it's sent to the CMS.
var summary = await _newsService.GenerateSummaryAsync(article.Content);
summary = await ChatMeAsyncFull(
$"Please provide a 3-paragraph article about this content: {summary}"
);
A static method handles OpenAI API calls for generating natural language content.
public static async Task<string> ChatMeAsyncFull(string question)
{
// HTTP request to OpenAI's GPT-4 model
// Returns natural language summary
}
This design allows content to be refreshed automatically every few hours, creating a hands-free content creation pipeline with intelligent summarization.
Conclusion
The AI News Automation system represents a modern, modular approach to content automation. With clean service layers, real-time scheduling, and AI integration, it sets a standard for intelligent publishing workflows. Each component is isolated for clarity, testability, and maintainability—an architecture well-suited for scalable deployment.
The implementation combines the power of .NET, HtmlAgilityPack, and AI tools like OpenAI to provide a complete solution for auto-generating high-quality, engaging content. It can be easily extended to support multimedia integration, multilingual output, and advanced NLP enhancements.
Whether you're building a tech blog, an internal news aggregator, or a full-fledged digital publication, this system offers a solid, adaptable starting point for intelligent content automation.