News Aggregator: This component is responsible for fetching news articles from multiple online sources such as websites, RSS feeds, APIs, etc. It uses web crawlers or scrapers to collect the content in real-time or periodically based on user preferences and requirements. The aggregated data can be stored in a database for further processing.

Data Processing: This component takes the raw news articles collected by the News Aggregator, parses them into structured data (such as JSON), and removes any unnecessary formatting or elements. It may also involve cleaning up the text to remove HTML tags, advertisements, and other unwanted content.

Sentiment Analysis: This component analyzes the sentiment of each news article using natural language processing techniques such as machine learning algorithms or pre-trained models like BERT. The output can be a positive, negative, or neutral score that represents the overall sentiment of the article.

Summary Generation: Using the processed data and sentiment scores, this component generates short summaries for each news article. This can involve employing techniques such as text summarization algorithms (e.g., LSTM-based models) to create a concise summary that captures the essence of the original content while maintaining its context.

Commentary Generation: The Commentary Generator takes the summaries and sentiment scores, along with other relevant data sources such as user preferences or trending topics, to generate commentary on each news article. This can be done using natural language generation techniques that combine machine learning models and rule-based systems to create human-like text.

User Interface: The User Interface component is responsible for presenting the summaries and commentaries generated by previous components in an organized, visually appealing manner. It may include features such as search functionality, filtering options (e.g., by sentiment or topic), and sorting capabilities to help users navigate through the content easily.

Database: The Database component stores all collected news articles, their summaries, commentaries, and related metadata for future reference and analysis. It can be a relational database like MySQL or NoSQL databases such as MongoDB or Cassandra, depending on the requirements of the system.

Administration: This component includes tools and interfaces to manage the web service, such as monitoring its performance, updating configurations, adding new data sources, and managing user accounts. It may also include features for analyzing user behavior and engagement with the content, which can be used to improve the overall system's functionality and relevance.

1. Content Moderation: To ensure that the chatbot provides accurate, reliable, and unbiased information to users, there should be a system in place for monitoring and filtering news sources. This could involve employing human moderators or using AI-driven algorithms to identify fake news, misinformation, and biases from various sources.

2. User Engagement: A successful news aggregator chatbot relies on user engagement and satisfaction. To achieve this, the chatbot should have a feedback mechanism that allows users to rate content, provide suggestions for improvement, or report any issues they encounter while using it. This data can then be used by developers to make necessary adjustments and improvements.

3. Community Guidelines: A set of community guidelines would outline the rules and expectations for both chatbot users and administrators. These guidelines could cover topics such as respectful behavior, avoiding spam or inappropriate content, and adhering to copyright laws. By establishing these guidelines, a sense of order and accountability can be maintained within the news aggregator community.

4. Transparency: To build trust with users, it's essential for the chatbot to provide information about its sources, moderation processes, and any potential conflicts of interest. This transparency allows users to make informed decisions about which content they choose to engage with and helps maintain credibility within the news aggregator ecosystem.

5. Regular Updates: As technology advances and user preferences change, it's crucial for the chatbot to undergo regular updates and improvements. By staying up-to-date with advancements in AI, natural language processing, and other relevant technologies, the chatbot can continue to provide a high-quality news aggregation service that meets users' needs.

6. Collaborative Governance: Involving stakeholders such as journalists, media organizations, and technology experts in the governance process ensures diverse perspectives are considered when making decisions about content moderation policies or feature updates. This collaborative approach promotes a more inclusive and well-rounded news aggregator experience for users.

In summary, a successful governance system for a news aggregator chatbot would involve monitoring and filtering content, engaging with users to improve the service, establishing community guidelines, maintaining transparency, regularly updating the platform, and collaborating with various stakeholders in decision-making processes.