Best AI tools for< Text Summarization >

Brevity is an AI tool designed to transform long-form content into clear and concise summaries. By utilizing advanced natural language processing algorithms, Brevity helps users save time by providing quick and accurate summaries of lengthy texts. With a user-friendly interface, Brevity is suitable for professionals, students, and anyone looking to streamline their reading and research process.

Tinq.ai is a natural language processing (NLP) tool that provides a range of text analysis capabilities through its API. It offers tools for tasks such as plagiarism checking, text summarization, sentiment analysis, named entity recognition, and article extraction. Tinq.ai's API can be integrated into applications to add NLP functionality, such as content moderation, sentiment analysis, and text rewriting.

Rytar is an AI-powered writing platform that helps users generate unique, relevant, and high-quality content in seconds. It uses state-of-the-art AI writing models to generate articles, blog posts, website pages, and other types of content from just a headline or a few keywords. Rytar is designed to help users save time and effort in the content creation process, and to produce content that is optimized for SEO and readability.

This website provides a suite of AI-powered writing tools, including an AI writer, text summarizer, story writer, and headline generator. These tools can help you generate high-quality content quickly and easily.

Cognify Insights is an AI-powered research assistant that provides instant understanding of online content such as text, diagrams, tables, and graphs. With a simple drag and drop feature, users can quickly analyze any type of content without leaving their browsing tab. The tool offers valuable insights and helps users unlock information efficiently.

TLDR This is an online article summarizer tool that helps users quickly understand the essence of lengthy content. It uses AI to analyze any piece of text and summarize it automatically, in a way that makes it easy to read, understand, and act on. TLDR This also extracts essential metadata such as author and date information, related images, and the title. Additionally, it estimates the reading time for news articles and blog posts, ensuring users have all the necessary information consolidated in one place for efficient reading. TLDR This is designed for students, writers, teachers, institutions, journalists, and any internet user who needs to quickly understand the essence of lengthy content.

The website offers a free online text summarizer powered by AI, designed to condense lengthy texts efficiently. It caters to professionals, students, and researchers who need to extract key details from documents. The tool utilizes advanced AI algorithms to provide users with essential information quickly, enhancing learning and productivity. Users can easily summarize text by pasting it into the tool, generating clear and concise summaries for research or quick information retrieval. The AI-enhanced tool aims to improve efficiency in processing large volumes of text.

InfraNodus is a text network visualization tool that helps users generate insights from any discourse by representing it as a network. It uses AI-powered algorithms to identify structural gaps in the text and suggest ways to bridge them. InfraNodus can be used for a variety of purposes, including research, creative writing, marketing, and SEO.

Summarizer AI is a free online tool that simplifies and condenses extensive text documents, articles, or any written content into concise, easily digestible summaries. This cutting-edge artificial intelligence (AI) technology aims to enhance productivity and comprehension by breaking down complex information into its most essential points, making it particularly useful for students, researchers, professionals, and anyone looking to quickly grasp the main ideas of lengthy texts. The platform is user-friendly, emphasizing privacy and security for its users. It enhances reading comprehension by highlighting key terms and facilitates efficient knowledge acquisition without compromising on data confidentiality. Summarizer AI stands out for its versatility, ease of use, and commitment to user privacy, making it an invaluable resource for efficient text analysis and summarization.

UpSum is a text summarization tool that uses advanced AI technology to condense lengthy texts into concise summaries. It is designed to save users time and effort by extracting the key points and insights from documents, research papers, news articles, and other written content. UpSum's AI algorithm analyzes the text, identifies the most important sentences and phrases, and assembles them into a coherent summary that accurately represents the main ideas and key takeaways of the original text. The tool is easy to use, simply upload or paste your text, select the desired summary length, and click the summarize button. UpSum is available as a free web-based tool, as well as a premium subscription with additional features and capabilities.

TLDRai.com is an AI tool designed to help users summarize any text into concise and easy-to-digest content, enabling them to free themselves from information overload. The tool utilizes AI technology to provide efficient text summarization services, making it a valuable resource for individuals seeking quick and accurate summaries of lengthy texts.

The AI Summarizing Tool is a free online summary generator that uses advanced AI technology to quickly identify important points in text while maintaining the original context. Users can summarize various types of content such as articles, paragraphs, essays, and thesis. The tool is developed with algorithms that ensure accuracy and efficiency in creating summaries without altering the original meaning of the content. It offers features like free usage, unlimited text summarization, support for multiple languages, integration with other writing tools, and precise summaries using AI technology.

AI Summarizer is an online tool that uses state-of-the-art AI technology to shorten text while preserving all main points. It ensures accuracy and maintains original context, making it suitable for various types of content like essays or blog posts. Users can easily summarize text by typing or uploading content, with options to download, copy, or clear the summary. The tool offers features like setting summary length, showing bullets and best lines, and supporting multiple languages. It is known for its extensive word count limit, data safety, and readability-enhancing features.

Smart-Summarizer is a powerful AI-powered tool that helps you summarize text quickly and easily. With its advanced algorithms, Smart-Summarizer can automatically extract the most important points from any piece of text, creating a concise and informative summary in seconds. Whether you're a student trying to condense your notes, a researcher needing to synthesize complex information, or a professional looking to save time on reading lengthy documents, Smart-Summarizer is the perfect tool for you.

Note Summarizer is an AI-powered tool that helps you quickly and easily summarize large amounts of text. With its advanced natural language processing capabilities, Note Summarizer can extract the most important points from any document, article, or website, providing you with a concise and informative summary in seconds.

SummaryGenerator.io is an AI-powered text summarizer that uses advanced algorithms and natural language processing to analyze the content and identify main ideas to generate relevant summaries. It generates summaries of varying lengths for any type of content.

Clarifai is a full-stack AI developer platform that provides a range of tools and services for building and deploying AI applications. The platform includes a variety of computer vision, natural language processing, and generative AI models, as well as tools for data preparation, model training, and model deployment. Clarifai is used by a variety of businesses and organizations, including Fortune 500 companies, startups, and government agencies.

ContextClue is an AI text analysis tool that offers enhanced document insights through features like text summarization, report generation, and LLM-driven semantic search. It helps users summarize multi-format content, automate document creation, and enhance research by understanding context and intent. ContextClue empowers users to efficiently analyze documents, extract insights, and generate content with unparalleled accuracy. The tool can be customized and integrated into existing workflows, making it suitable for various industries and tasks.

SumyAI is an AI-powered tool that helps users get 10x faster insights from YouTube videos. It condenses lengthy videos into key points for faster absorption, saving time and enhancing retention. SumyAI also provides summaries of events and conferences, podcasts and interviews, educational tutorials, product reviews, news reports, and entertainment.

Summate.it is a tool that uses OpenAI to quickly summarize web articles. It is simple and clean, and it can be used to summarize any web article by simply pasting the URL into the text box. Summate.it is a great way to quickly get the gist of an article without having to read the entire thing.

DistiLlama is a Chrome extension that leverages a locally running Large Language Model (LLM) to perform various tasks, including text summarization, chat, and document analysis. It utilizes Ollama as the locally running LLM instance and LangChain for text summarization. DistiLlama provides a user-friendly interface for interacting with the LLM, allowing users to summarize web pages, chat with documents (including PDFs), and engage in text-based conversations. The extension is easy to install and use, requiring only the installation of Ollama and a few simple steps to set up the environment. DistiLlama offers a range of customization options, including the choice of LLM model and the ability to configure the summarization chain. It also supports multimodal capabilities, allowing users to interact with the LLM through text, voice, and images. DistiLlama is a valuable tool for researchers, students, and professionals who seek to leverage the power of LLMs for various tasks without compromising data privacy.

LLPhant is a comprehensive PHP Generative AI Framework that provides a simple and powerful way to build apps. It supports Symfony and Laravel and offers a wide range of features, including text generation, chatbots, text summarization, and more. LLPhant is compatible with OpenAI and Ollama and can be used to perform a variety of tasks, including creating semantic search, chatbots, personalized content, and text summarization.

PPL LLM Serving is a serving based on ppl.nn for various Large Language Models (LLMs). It provides inference support for LLaMA. Key features include: * **High Performance:** Optimized for fast and efficient inference on LLM models. * **Scalability:** Supports distributed deployment across multiple GPUs or machines. * **Flexibility:** Allows for customization of model configurations and inference pipelines. * **Ease of Use:** Provides a user-friendly interface for deploying and managing LLM models. This tool is suitable for various tasks, including: * **Text Generation:** Generating text, stories, or code from scratch or based on a given prompt. * **Text Summarization:** Condensing long pieces of text into concise summaries. * **Question Answering:** Answering questions based on a given context or knowledge base. * **Language Translation:** Translating text between different languages. * **Chatbot Development:** Building conversational AI systems that can engage in natural language interactions. Keywords: llm, large language model, natural language processing, text generation, question answering, language translation, chatbot development

LangChain Decoded is an open-source framework designed to facilitate the development of applications utilizing large language models (LLMs). It can be applied to tasks such as chatbots, text summarization, data generation, code understanding, question answering, and evaluation. The framework consists of various modules like Models, Embeddings, Prompts, Indexes, Memory, Chains, Agents, and Callbacks, each explored in separate Python notebooks. Users can follow the blog post series to understand and utilize LangChain for their projects.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

eShopSupport is a sample .NET application showcasing common use cases and development practices for building AI solutions in .NET, specifically Generative AI. It demonstrates a customer support application for an e-commerce website using a services-based architecture with .NET Aspire. The application includes support for text classification, sentiment analysis, text summarization, synthetic data generation, and chat bot interactions. It also showcases development practices such as developing solutions locally, evaluating AI responses, leveraging Python projects, and deploying applications to the Cloud.

Intel® Extension for Transformers is an innovative toolkit designed to accelerate GenAI/LLM everywhere with the optimal performance of Transformer-based models on various Intel platforms, including Intel Gaudi2, Intel CPU, and Intel GPU. The toolkit provides the below key features and examples: * Seamless user experience of model compressions on Transformer-based models by extending [Hugging Face transformers](https://github.com/huggingface/transformers) APIs and leveraging [Intel® Neural Compressor](https://github.com/intel/neural-compressor) * Advanced software optimizations and unique compression-aware runtime (released with NeurIPS 2022's paper [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) and [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114), and NeurIPS 2021's paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754)) * Optimized Transformer-based model packages such as [Stable Diffusion](examples/huggingface/pytorch/text-to-image/deployment/stable_diffusion), [GPT-J-6B](examples/huggingface/pytorch/text-generation/deployment), [GPT-NEOX](examples/huggingface/pytorch/language-modeling/quantization#2-validated-model-list), [BLOOM-176B](examples/huggingface/pytorch/language-modeling/inference#BLOOM-176B), [T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), [Flan-T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), and end-to-end workflows such as [SetFit-based text classification](docs/tutorials/pytorch/text-classification/SetFit_model_compression_AGNews.ipynb) and [document level sentiment analysis (DLSA)](workflows/dlsa) * [NeuralChat](intel_extension_for_transformers/neural_chat), a customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of [plugins](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat/docs/advanced_features.md) such as [Knowledge Retrieval](./intel_extension_for_transformers/neural_chat/pipeline/plugins/retrieval/README.md), [Speech Interaction](./intel_extension_for_transformers/neural_chat/pipeline/plugins/audio/README.md), [Query Caching](./intel_extension_for_transformers/neural_chat/pipeline/plugins/caching/README.md), and [Security Guardrail](./intel_extension_for_transformers/neural_chat/pipeline/plugins/security/README.md). This framework supports Intel Gaudi2/CPU/GPU. * [Inference](https://github.com/intel/neural-speed/tree/main) of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels for Intel CPU and Intel GPU (TBD), supporting [GPT-NEOX](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox), [LLAMA](https://github.com/intel/neural-speed/tree/main/neural_speed/models/llama), [MPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/mpt), [FALCON](https://github.com/intel/neural-speed/tree/main/neural_speed/models/falcon), [BLOOM-7B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/bloom), [OPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/opt), [ChatGLM2-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/chatglm), [GPT-J-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptj), and [Dolly-v2-3B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox). Support AMX, VNNI, AVX512F and AVX2 instruction set. We've boosted the performance of Intel CPUs, with a particular focus on the 4th generation Intel Xeon Scalable processor, codenamed [Sapphire Rapids](https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors.html).

Project Lakechain is a cloud-native, AI-powered framework for building document processing pipelines on AWS. It provides a composable API with built-in middlewares for common tasks, scalable architecture, cost efficiency, GPU and CPU support, and the ability to create custom transform middlewares. With ready-made examples and emphasis on modularity, Lakechain simplifies the deployment of scalable document pipelines for tasks like metadata extraction, NLP analysis, text summarization, translations, audio transcriptions, computer vision, and more.

AutoGPTQ is an easy-to-use LLM quantization package with user-friendly APIs, based on GPTQ algorithm (weight-only quantization). It provides a simple and efficient way to quantize large language models (LLMs) to reduce their size and computational cost while maintaining their performance. AutoGPTQ supports a wide range of LLM models, including GPT-2, GPT-J, OPT, and BLOOM. It also supports various evaluation tasks, such as language modeling, sequence classification, and text summarization. With AutoGPTQ, users can easily quantize their LLM models and deploy them on resource-constrained devices, such as mobile phones and embedded systems.

This repository contains a collection of apps powered by LangChain, an open-source framework designed to aid the development of applications leveraging large language models (LLMs). It can be used for various tasks such as chatbots, text summarisation, data generation, code understanding, question answering, and evaluation. The repository showcases different applications built using LangChain and other tools like OpenAI, Chroma, Gemini, Helicone, Serper API, Pinecone, and Tavily Search API.

LLM-PlayLab is a repository containing various projects related to LLM (Large Language Models) fine-tuning, generative AI, time-series forecasting, and crash courses. It includes projects for text generation, sentiment analysis, data analysis, chat assistants, image captioning, and more. The repository offers a wide range of tools and resources for exploring and implementing advanced AI techniques.

LangTest is a comprehensive evaluation library for custom LLM and NLP models. It aims to deliver safe and effective language models by providing tools to test model quality, augment training data, and support popular NLP frameworks. LangTest comes with benchmark datasets to challenge and enhance language models, ensuring peak performance in various linguistic tasks. The tool offers more than 60 distinct types of tests with just one line of code, covering aspects like robustness, bias, representation, fairness, and accuracy. It supports testing LLMS for question answering, toxicity, clinical tests, legal support, factuality, sycophancy, and summarization.

This repository contains course materials for T81 559: Applications of Generative Artificial Intelligence at Washington University in St. Louis. The course covers practical applications of Large Language Models (LLMs) and text-to-image networks using Python. Students learn about generative AI principles, LangChain, Retrieval-Augmented Generation (RAG) model, image generation techniques, fine-tuning neural networks, and prompt engineering. Ideal for students, researchers, and professionals in computer science, the course offers a transformative learning experience in the realm of Generative AI.

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

Eden AI aims to simplify the use and deployment of AI technologies by providing a unique API that connects to all the best AI engines. With the rise of **AI as a Service** , a lot of companies provide off-the-shelf trained models that you can access directly through an API. These companies are either the tech giants (Google, Microsoft , Amazon) or other smaller, more specialized companies, and there are hundreds of them. Some of the most known are : DeepL (translation), OpenAI (text and image analysis), AssemblyAI (speech analysis). There are **hundreds of companies** doing that. We're regrouping the best ones **in one place** !

This repository provides a set of ROS 2 packages to integrate llama.cpp into ROS 2. By using the llama_ros packages, you can easily incorporate the powerful optimization capabilities of llama.cpp into your ROS 2 projects by running GGUF-based LLMs and VLMs.

Hands-On-LLM-Applications-Development is a repository focused on developing applications using Large Language Models (LLMs). The repository provides hands-on tutorials, guides, and resources for building various applications such as LangChain for LLM applications, Retrieval Augmented Generation (RAG) with LangChain, building LLM agents with LangGraph, and advanced LangChain with OpenAI. It covers topics like prompt engineering for LLMs, building applications using HuggingFace open-source models, LLM fine-tuning, and advanced RAG applications.

This repository contains projects and notes on common practical techniques for fine-tuning Large Language Models (LLMs). It includes fine-tuning LLM notebooks, Colab links, LLM techniques and utils, and other smaller language models. The repository also provides links to YouTube videos explaining the concepts and techniques discussed in the notebooks.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

Txtai is an all-in-one embeddings database for semantic search, LLM orchestration, and language model workflows. It combines vector indexes, graph networks, and relational databases to enable vector search with SQL, topic modeling, retrieval augmented generation, and more. Txtai can stand alone or serve as a knowledge source for large language models (LLMs). Key features include vector search with SQL, object storage, topic modeling, graph analysis, multimodal indexing, embedding creation for various data types, pipelines powered by language models, workflows to connect pipelines, and support for Python, JavaScript, Java, Rust, and Go. Txtai is open-source under the Apache 2.0 license.

Text zusammenfassen spart Zeit und extrahiert die Kernaussagen. Nutzen Sie unseren Service, um Texte effektiv zu zusammenfassen.

I create formal, one-sentence summaries from text blocks.

summarizes text

BIAN v12.0

Summarizes texts and images into clear, concise summaries.

发给GPT一篇markdown格式或者文本格式的文章，该GPT将帮助你提炼该文章并以30字作为总结摘要，同时将生成主题相关图。

Summarizes texts in various styles so they can be effective to various stakeholders

Refines business texts for clarity and professionalism, with summaries.

AI search engine with summarization.

Summarizes articles in points; asks to create minimal-text visuals.

Advanced AI for in-depth analysis and summarization of articles.

Friendly copywriter for blogs, using provided text, with Japanese support and summaries.

Nyimpulkeun sareng nimba poin konci tina téks, artikel, video, dokumén sareng seueur deui

Summarize to Memorize

Expert in summarizing and categorizing

Summarizes texts in various languages, matching user's language.

Ce GPT va résumer votre texte pour ne garder que l'essentiel. Il supporte aussi les liens (à condition que le site ne s'y soit pas opposé)

Paste your notes in here and I'll re-organizes your hastily written notes, write you a summary and give you actionable insights.

Curated content creator for language learning. Read what you want in ANY language.

Summarizes academic books by chapters or segments, with user prompts.