Smmry

Reduces long-form text content (articles, documents, web pages) into concise summaries using extractive or abstractive summarization algorithms. The system analyzes semantic importance and sentence relevance scores to identify key information, then compresses content while preserving meaning. Users can control summary length via a percentage slider (typically 10-100% of original length), allowing trade-offs between brevity and detail retention.

Accepts URLs as input and automatically fetches, parses, and summarizes web page content in a single operation. The system performs HTTP requests to retrieve HTML, applies DOM parsing and text extraction to isolate article body content (filtering navigation, ads, sidebars), then applies summarization algorithms. This eliminates manual copy-paste workflows and handles dynamic content loading for most standard web pages.

Provides a user-facing parameter (typically a percentage slider from 10-100%) that controls the compression ratio of summarization output without requiring re-processing or model retraining. The system uses this parameter to adjust sentence selection thresholds or token budgets in the summarization algorithm, allowing users to trade off between brevity and information retention on-the-fly.

Exposes a programmatic API endpoint that accepts multiple URLs in a single request and returns summaries for all URLs in batch, enabling integration into workflows, scripts, and third-party applications. The API handles concurrent fetching and summarization of multiple pages, returning structured JSON responses with metadata, original content, and summaries for each URL.

Provides a browser extension (Chrome, Firefox, Safari) that injects a summarization UI directly into web pages, allowing users to summarize the current page without leaving the browser or copying content. The extension communicates with Smmry's backend API to process the page's DOM content and displays results in a sidebar or modal overlay, with options to adjust summary length and export results.

Supports summarization of content in multiple languages (typically 10-50+ languages) by detecting input language automatically or accepting explicit language parameters. The system applies language-specific NLP preprocessing (tokenization, stopword removal, stemming) and may use multilingual models or language-specific summarization algorithms to preserve semantic meaning across linguistic boundaries.

Returns the original document with key sentences highlighted or marked, allowing users to see which sentences the summarization algorithm identified as most important. This provides transparency into the summarization process and enables users to understand the semantic importance scoring without reading the full summary. The implementation typically uses CSS styling or HTML markup to highlight sentences in the original text.