/**
 *  ### HTML-to-Main-Content Extractor #2
 *
 * 1. The algorithm starts by loading the HTML content using linkedom, a lightweight DOM parser for Node.js.
 * 2. It then applies a series of cleaning and scoring techniques to identify the main content of
 * the page, starting with stripping unlikely candidates (e.g., elements with class names like "comment"
 *  or "sidebar").
 * 3. The HTML is converted into a series of paragraph elements, which are then scored based on various
 *  factors such as text length, number of commas, and the presence of certain class names or IDs.
 * 4. The algorithm assigns scores to parent and grandparent elements based on the scores of their
 * children, with parents receiving the full score and grandparents receiving half.
 * 5. After scoring, the algorithm finds the top candidate element by selecting the node with the
 * highest score.
 * 6. The top candidate's siblings are then examined to see if they should be included in the main
 * content, based on their scores and other factors like link density.
 * 7. The algorithm then cleans the selected content by removing unnecessary tags, attributes, and empty
 * elements.
 * 8. It also handles special cases like cleaning up header tags, images, and other potentially irrelevant
 *  content.
 * 9. Throughout the process, the algorithm uses various regular expressions and scoring heuristics to
 * identify positive and negative indicators of content relevance.
 * 10. Finally, the cleaned and extracted content is returned as an HTML string, representing the main
 * body of the article or webpage.
 *
 * [Article Extraction Benchmark](https://trafilatura.readthedocs.io/en/latest/evaluation.html)
 *
 * @param {string} html - The HTML content to extract from.
 * @param {Object} [opts] - The options for content extraction.
 * @param {boolean} opts.stripUnlikelyCandidates default=true - Remove elements that match non-article-
 * like criteria first (e.g., elements with a classname of "comment").
 * @param {boolean} opts.weightNodes default=true - Modify an element's score based on certain classNames or
 * IDs (e.g., subtract if a node has a className of 'comment', add if a node has an ID of 'entry-content').
 * @param {boolean} opts.cleanConditionally default=true - Clean the node to remove superfluous content
 *  like forms, ads, etc. Initially, pass in the most restrictive options which will return the highest
 * quality content. On each failure, retry with slightly more lax options.
 * @returns {string} The extracted content as an HTML string, or null if extraction fails.
 * @author [vtempest (2025)](https://github.com/vtempest)
 * Based on [Postlight Mercury Parser (2017-)](https://github.com/postlight/parser/tree/main/src)
 * @example var url =  "https://en.wikipedia.org/wiki/David_Hilbert"
 * var html = await (await fetch(url)).text();
 * var content = extractMainContentFromHTML(html);
 * console.log(content); // HTML content of main article body
 * @category Extract
 */
export declare function extractMainContentFromHTML2(html: any, opts: any): any;
/**
 * Sets the score attribute of a node.
 * @param {Node} node - The node to set the score on.
 * @param {Document} document - The document object.
 * @param {number} score - The score to set.
 * @returns {Node} The node with the set score.
 * @private
 */
export declare function setScore(node: any, document: any, score: any): any;
/**
 * Scores a paragraph node.
 * @param {Node} node - The paragraph node to score.
 * @private
 * @returns {number} The score of the paragraph.
 */
export declare function scoreParagraph(node: any): number;