A deep dive into our dual-model approach: combining precise Extractive Summarization algorithms with ML reasoning.
Uses TF-IDF to turn sentences into numbers that show how important the words are. Logistic Regression then learns from labeled examples to decide whether a sentence is important or not, helping the system select the most useful sentences for summarization.
By training on labeled datasets where "gold standard" summaries exist, our Logistic Regression model learns specific feature weights—such as sentence position, length, and keyword density—that denote high-value information.
P(y=1|x) = 1 / (1 + e^(-(β0 + β1x1 + ... + βnxn)))
When no training data is available, we treat the document as a connected graph. Sentences are nodes, and similarity scores are edges. The most "connected" sentences are mathematically determined to be central to the topic using the PageRank algorithm.
Uses TF-IDF to turn sentences into numerical features that show the importance of their words. Then, TextRank looks at how sentences are connected and ranks them to find the most important ones.
While our extractive models provide factual summaries, the Gemini API adds a layer of reasoning. It allows users to query the document contextually and translate findings instantly.
End-to-End Processing Pipeline
User uploads PDF/Text/Image and selects summary length and tone.
ML Model Selection (Supervised/Unsupervised), Text Cleaning, and Ranking.
Split view output, PDF export, Gemini-powered translation and chat.