Search Results for "summarization"
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Showing 165 open source projects for "summarization"
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OpenAI Translator
Browser extension and cross-platform desktop app based on ChatGPT API
...The desktop application does not support the pop-up icon after word selection. You must press the shortcut key to trigger the translation after selecting a word. It offers three modes: translation, polishing and summarization. Our tool allows for mutual translation, polishing and summarization across 55 different languages. Streaming mode is supported! It allows users to customize their translation text. One-click copying, Text-to-Speech (TTS). Available on all platforms (Windows, macOS, and Linux) for both browsers and Desktop. -
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Readest
Readest is a modern, feature-rich ebook reader
...Although the repository is not as widely documented or popular as some, the idea is that Readest supports features to help with reading comprehension — likely combining OCR / text retrieval, translation, note-taking, or summarization for reading materials (eBooks, articles, PDFs). The goal appears to be to let users feed in arbitrary reading material and then interact with it (highlighting, translation, lookup, maybe TTS or summarization) more comfortably. Because of that, it's oriented towards learners, researchers, or people dealing with multilingual documents — especially when they need to rapidly digest or reference large amounts of text. ... -
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Automatic text summarizer
Module for automatic summarization of text documents and HTML pages
Sumy is an automatic text summarization library that provides multiple algorithms for extracting key content from documents and articles. Simple library and command line utility for extracting summary from HTML pages or plain texts. The package also contains a simple evaluation framework for text summaries. Implemented summarization methods are described in the documentation. -
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LLM TLDR
95% token savings. 155x faster queries. 16 languages
LLM TLDR is a tool that leverages large language models (LLMs) to generate concise, coherent summaries (TL;DRs) of long documents, articles, or text files, helping users quickly understand large amounts of content without reading every word. It integrates with LLM APIs to handle input texts of varying lengths and complexity, applying techniques like chunking, context management, and multi-pass summarization to preserve accuracy even when the source is very large. The system supports both extractive and abstractive summarization styles so that users can choose whether they want condensed highlights or a more narrative paraphrase of key ideas. To enhance usability, LLM-TLDR includes command-line tools and integration examples for common workflows like batch summarization, webhook ingestion, and automation in documentation pipelines. -
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nashsu LLM Wiki
LLM Wiki is a cross-platform desktop application
...The system emphasizes discoverability, allowing users to navigate topics through links and relationships rather than static search results. It may include mechanisms for content generation, summarization, and contextual linking, making it useful for research and knowledge management. The project reflects the trend of combining LLM capabilities with traditional information architectures. Overall, it provides a dynamic alternative to conventional documentation systems. -
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Scriberr
Self-hosted AI audio transcription
...The application includes a polished user interface that simplifies the management of recordings, transcripts, and annotations, making it suitable for both casual users and professionals handling large volumes of audio. Beyond transcription, Scriberr also integrates features such as summarization, tagging, and interaction with language models, allowing users to extract insights from conversations or meetings efficiently. -
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PaperQA2
High accuracy RAG for answering questions from scientific documents
PaperQA2 is a package for doing high-accuracy retrieval augmented generation (RAG) on PDFs or text files, with a focus on the scientific literature. See our recent 2024 paper to see examples of PaperQA2's superhuman performance in scientific tasks like question answering, summarization, and contradiction detection. In this example we take a folder of research paper PDFs, magically get their metadata - including citation counts and a retraction check, then parse and cache PDFs into a full-text search index, and finally answer the user question with an LLM agent. -
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Python Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, source code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. ... -
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Claude Autoresearch
Claude Autoresearch Skill, autonomous goal-directed iteration
...Its iterative loop enables deeper exploration of topics over time, making it particularly useful for complex or open-ended research questions. The architecture emphasizes autonomy, reducing the need for constant user input while still producing meaningful insights. It may also include summarization and reporting capabilities to present findings in a digestible format. Overall, autoresearch represents a step toward self-directed knowledge discovery systems that continuously improve their outputs through iteration. -
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Node.js Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models
This is the Node.js client (with Typescript types) for the NLP Cloud API. NLP Cloud serves high-performance pre-trained or custom models for NER, sentiment analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, text generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. ... -
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Rocketnotes
AI-powered markdown editor - leverage LLMs with your documents
...The system is designed with productivity workflows in mind, allowing users to build interconnected notes and maintain organized collections of information. By combining note management with AI-assisted writing and summarization tools, the platform aims to help users process large volumes of information more efficiently. -
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KaraKeep
A self-hostable bookmark-everything app
KaraKeep is a self-hostable “bookmark everything” application that lets users save and organize links, notes, images, and documents in one place with rich metadata and AI-assisted tagging. Built with a focus on self-hosting and personal control, Karakeep integrates full-text search and AI-based automatic tagging and summarization (supporting local models like Ollama) to quickly make large collections navigable. Users can group saved items into lists, collaborate with others, and organize content with custom tags and filters. Automatic fetching of link titles, descriptions, and images streamlines saving content without manual edits, while rule-based management lets users define customized workflows. ... -
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MemPalace
The highest-scoring AI memory system ever benchmarked
MemPalace is an open-source AI memory system designed to solve one of the most persistent limitations of large language models: the loss of context between sessions. Instead of relying on summarization or selective extraction like most memory tools, it takes a radically different approach by storing conversations in their entirety and making them retrievable through structured organization and semantic search. The system is inspired by the classical “memory palace” mnemonic technique, organizing information into hierarchical spaces such as wings, rooms, and halls, which allows AI agents to navigate past knowledge in a more contextual and intuitive way. ... -
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LLM Wiki
Open Source Implementation of Karpathy's LLM Wiki
LLM Wiki is a knowledge management and documentation system designed to organize, generate, and maintain structured information using large language models. It allows users to create interconnected knowledge bases that function similarly to a wiki but are enhanced with AI-driven content generation and summarization. The system emphasizes linking and context, enabling information to be connected across pages and topics for better navigation and understanding. It likely includes features for automatic content updates, ensuring that information remains relevant as new data becomes available. The architecture supports both manual editing and automated generation, providing flexibility in how knowledge is curated. ... -
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doccano
Open source annotation tool for machine learning practitioners
...It provides annotation features for text classification, sequence labeling and sequence-to-sequence tasks. So, you can create labeled data for sentiment analysis, named entity recognition, text summarization and so on. Just create a project, upload data and start annotating. You can build a dataset in hours. -
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MarkFlowy
The AI Markdown Editor
...Built using technologies like Tauri, it offers a fast and efficient desktop experience while maintaining a small footprint compared to traditional editors. The application integrates AI assistants that support tasks such as text generation, translation, summarization, and conversational interaction, helping users improve productivity and content quality. It supports multiple editing modes, including both raw Markdown and WYSIWYG interfaces, allowing users to choose their preferred workflow. MarkFlowy also includes a robust file management system with features like global search, drag-and-drop organization, and support for multiple file types beyond Markdown, such as JSON and plain text. ... -
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Supermemory
Memory engine and app that is extremely fast, scalable
...The platform allows individuals to ingest text, documents, and other content forms, then uses advanced retrieval and embedding techniques to index and relate information intelligently so that users can recall relevant knowledge in context rather than just by keyword match. It often incorporates clustering, semantic search, and summarization modules to reduce cognitive load and surface key ideas, which makes it useful for research, study, writing, and long-term project tracking. Users can interact with the system via conversational queries or traditional search interfaces, and the system leverages vector embeddings and memory scoring to prioritize the most relevant results. -
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Lossless Claw
LCM (Lossless Context Management) plugin for OpenClaw
Lossless Claw is an advanced context management plugin for the OpenClaw agent ecosystem that redefines how conversational memory is handled in large language model systems. Instead of relying on traditional sliding-window truncation or lossy summarization, it introduces a lossless architecture that preserves all historical messages while maintaining usable context within token limits. The system stores every interaction in a persistent database and incrementally summarizes older content into a hierarchical directed acyclic graph, allowing efficient compression without discarding information. ... -
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tldw Server
Your Personal Research Multi-Tool
...The server component typically acts as the core infrastructure that manages summaries, metadata, and retrieval operations for client applications or user interfaces. In practical deployments, a system like this can support AI-powered summarization pipelines that process transcripts, articles, or other long-form material and store condensed versions for easier consumption. The mirrored project hosted on SourceForge exists to preserve the availability of the code and provide an alternative download location for developers and researchers. Such servers are commonly integrated with AI models that generate summaries and tag content automatically. -
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PHP Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. ... -
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Claude Cookbooks
A collection of notebooks/recipes showcasing ways of using Claude
Claude Cookbooks is a curated collection of practical examples, notebooks, and implementation guides that demonstrate how to effectively use Claude’s API across a wide range of tasks. It serves as both a learning resource and a reference library, helping developers understand how to apply AI capabilities such as classification, summarization, and retrieval-augmented generation in real-world scenarios. The repository includes structured examples for integrating Claude with external tools, databases, and APIs, showcasing how to extend its functionality beyond basic text generation. It also covers advanced techniques like sub-agent orchestration, prompt optimization, and automated evaluation workflows. ... -
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ZeroGPU Router
Cut inference costs without dumbing down your agent
ZeroGPU Router is a task router for AI agents that sends simple, focused workloads to smaller specialized models. It is designed to reduce inference costs without replacing the main reasoning model used by the agent. The router exposes tools for tasks such as summarization, classification, PII redaction, entity extraction, JSON extraction, follow-up generation, and short chat. It works through MCP for OpenClaw and through a CLI plus plugin workflow for Claude Code. Each routed call can report useful metadata such as the selected model, latency, and estimated savings. Its main value is letting agents reserve expensive frontier models for complex reasoning while offloading routine language tasks to cheaper specialized routes. -
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NVIDIA AI Blueprint
Suite of reference architectures for building GPU-accelerated vision
...The project is organized around real-time video intelligence, downstream analytics, and agentic offline processing. It supports workflows such as natural-language video search, visual question answering, long-video summarization, clip retrieval, verified alerts, and incident analysis. It is designed for technical users who need deployable reference architectures for smart spaces, warehouse automation, SOP validation, monitoring, and operational video analytics. The repository includes Python agent code, Docker Compose deployment configurations, skills, scripts, and a Next.js-based UI. -
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Folo
Folo is the AI Reader
...The system also emphasizes user control through features like Bring Your Own Key support for AI providers and customizable feed management. Overall, Folo functions as an intelligent reading hub that blends classic RSS aggregation with adaptive AI filtering and summarization. -
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MCP YouTube
A Model-Context Protocol Server for YouTube
The YouTube MCP Server uses yt-dlp to download subtitles from YouTube videos and connects to claude.ai via the Model Context Protocol. It enables AI assistants to summarize YouTube videos by accessing their subtitles.
