Text Summarizer

Professional environments demand rapid information ingestion. This Text Summarizer utilizes statistical extraction logic to distill voluminous documents into their semantic core. Unlike generative AI, which may hallucinate facts, this tool operates on a strict selection principle: it retains the author's original sentences based on calculated information density.

The engine processes text through a multi-stage linguistic filter. First, it strips noise (stop words) using an extensive internal database. Next, it maps keyword frequency to establish a "Topic Signature." Sentences are then scored and ranked. The result is a mathematically precise reduction of the original material, preserving critical data points while eliminating redundancy. This local-processing architecture ensures zero data latency and absolute privacy for sensitive legal or corporate documents.

The algorithm determines sentence relevance (R) by aggregating the semantic weight of its keywords. The scoring function is defined as:

Where w(k) is the frequency coefficient of keyword k within the corpus. To prevent bias towards lengthy, rambling sentences, the score is normalized by the sentence length. A secondary boost factor β is applied to sentences containing transition markers (e.g., "consequently", "therefore") to preserve narrative logic.