Извлечение полезного контента страницы перед суммаризацией
Извлечение полезного контента страницы перед суммаризацией
Ответить самому
Сначала сформулируйте ответ как на собеседовании, затем откройте разбор и оцените себя.
Короткий ответ
Fetch the page, parse HTML, extract candidate text blocks with metadata, remove boilerplate using rules or a block classifier, then send only useful content to the summarizer.
Полный разбор
The first version can use standard web extraction libraries and heuristics: fetch HTML, parse DOM, remove scripts/styles/nav/footer, keep article-like headings and paragraphs, and preserve URL/title/source metadata. This is often enough for clean article pages.
For noisy pages, treat each DOM block as a candidate and classify whether it belongs to main content. Features can include tag type, text length, link density, position, heading proximity, boilerplate patterns and embeddings. A small supervised classifier or LLM-labeled dataset can bootstrap training, with human review for evaluation.
Do not dump the entire DOM into the LLM by default. It wastes tokens and increases hallucination risk. Keep a fallback path for pages where extraction confidence is low: ask the user, show partial extraction, or use a more expensive extraction model.
Теория
Summarization quality is bounded by content extraction quality; garbage in gives fluent garbage out.
Типичные ошибки
- Pass raw HTML or all visible text directly to the summarizer.
- Ignore boilerplate, cookie banners and navigation text.
- Train a block classifier without page-level evaluation.
- Drop headings and metadata that help preserve structure.
Как отвечать на собеседовании
- Mention link density and DOM-block classification.
- Describe a low-confidence fallback.