Automagically generate summaries from html or text.
TextRank javascript implementation for automatic text summarization
AI Text Summarization client library for JavaScript
Text summarization using Lexrank
A powerful React hook for text summarization using Google's Generative AI API. Easily integrate advanced text summarization capabilities into your React applications.
text summarization utility
A powerful text summarization package for Arabic and English content with sentiment analysis and topic extraction
A custom web component for text summarization
Text summarization based on sentence extraction for nodejs
Powerful text summarization algorithms from research papers and dedicated research.
A Flatfile plugin for text summarization and key phrase extraction
A fast and efficient Node.js AI-powered text summarization tool for concise and readable content, using frequency-based extractive NLP techniques.
An AI assistance and autocomplete extension for the Arkpad editor. It provides native commands for context-aware autocompletion and text summarization using custom AI request handlers.
Provides text summarization using the Bert Extractive Summarizer model.
Text summarization using Lexrank
Text summarization tool
Text summarization based on sentence extraction for nodejs
Text summarization based on sentence extraction for nodejs
Text summarization based on sentence extraction for nodejs
An isomorphic client library for the text analysis features in the Azure Cognitive Language Service.
PDF extraction and rendering across all JavaScript runtimes
xAI Grok adapter for TanStack AI chat, image generation, realtime, and structured outputs.
n8n node for integrating Palatine Speech API into workflow
## 1. Introduction
A function which takes text and summarizes it in 5 lines
Implementation of an extractive text summarization system which uses TF-IDF scores of words present in the text to rank sentences and generate a summary
Ruby C Extension for Open Text Summarizer
Ruby interface to libots libraries for unix.
A naive algorithm to summarize a piece of text
Identifies the most important sections of an article based on the presence of article-specific keywords.
A comprehensive Ruby gem for text translation, summarization, and multilingual content management using Mistral AI API. Features include context-aware translation, glossaries, batch processing, Rails integration with Mobility/Globalize support, monitoring, and advanced helpers for complex translation workflows.
An implementation of the Lexrank Algorithm, which summarize corpus of text documents.
Use webtagger to use keyword extraction web services (yahoo, tagthe and alchemy) to extract from a text terms suitable for tagging, summarization, query building, etc.
A Ruby gem that wraps the summarize CLI tool, providing a clean Ruby API for summarizing URLs, files, and text using various LLM providers.
A comprehensive Ruby gem that handles document processing, text extraction, and AI-powered analysis for PDF, Word, Excel, PowerPoint, images, archives, and more with a unified API. Includes agentic AI features for document analysis, summarization, and intelligent extraction.
Intuitive one-liner utility methods for common LLM tasks like text summarization, translation, data extraction, classification, grammar correction, sentiment analysis, key point extraction, text rewriting, and question answering.
The app provides a command-line interface (CLI) to an Ollama AI model, allowing users to engage in text-based conversations and generate human-like responses. Users can import data from local files or web pages, which are then processed through three different modes: fully importing the content into the conversation context, summarizing the information for concise reference, or storing it in an embedding vector database for later retrieval based on the conversation.
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, grammar and spelling correction, keywords and keyphrases extraction, chatbot, product description and ad generation, intent classification, text generation, image generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, speech synthesis, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API. This is the Ruby client for the API. More details here: https://nlpcloud.com. Documentation: https://docs.nlpcloud.com.
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