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23 dépôts

Awesome GitHub RepositoriesDocument Summarization

Automated methods for condensing long-form text into concise summaries.

Distinguishing note: Focuses on the summarization task specifically.

Explore 23 awesome GitHub repositories matching artificial intelligence & ml · Document Summarization. Refine with filters or upvote what's useful.

Awesome Document Summarization GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • anthropics/claude-cookbooksAvatar de anthropics

    anthropics/claude-cookbooks

    45,835Voir sur GitHub↗

    This repository serves as a comprehensive library of architectural blueprints and code examples for integrating large language models into software applications. It functions as a developer learning resource, providing structured tutorials and implementation patterns that demonstrate how to build intelligent features using advanced prompting and data processing techniques. The collection distinguishes itself by focusing on complex reasoning and data-grounding workflows. It provides practical guidance on implementing retrieval-augmented generation pipelines, which connect language models to pr

    Condenses lengthy articles or reports into concise summaries by identifying key points and extracting essential information.

    Jupyter Notebook
    Voir sur GitHub↗45,835
  • openai/openai-agents-pythonAvatar de openai

    openai/openai-agents-python

    27,191Voir sur GitHub↗

    This project is a Python framework for building autonomous, event-driven agent systems. It provides a unified runtime for orchestrating multi-agent workflows, managing persistent conversation state, and executing code within secure, isolated sandbox environments. The framework is designed to handle complex task delegation, allowing agents to invoke other agents as tools while maintaining context across multi-turn interactions. The framework distinguishes itself through its deep integration with the Model Context Protocol, enabling agents to connect to external data sources and remote services

    Condenses previous interaction history into concise formats to provide context when passing control between agents.

    Pythonagentsaiframework
    Voir sur GitHub↗27,191
  • ymcui/chinese-llama-alpacaAvatar de ymcui

    ymcui/Chinese-LLaMA-Alpaca

    18,944Voir sur GitHub↗

    This project is a comprehensive toolkit for adapting large language models to the Chinese language, providing a specialized framework for fine-tuning, inference, and local deployment. It serves as a coordinated suite for language-specific adaptation, including tools for expanding tokenizers and implementing retrieval-augmented generation. The project distinguishes itself through a complete pipeline for model adaptation, featuring multilingual tokenizer expansion and a fine-tuning framework that supports instruction-based supervised training and adapter merging. It also includes a dedicated de

    Includes capabilities for condensing long-form documents into concise summaries.

    Pythonalpacaalpaca-2large-language-models
    Voir sur GitHub↗18,944
  • piskvorky/gensimAvatar de piskvorky

    piskvorky/gensim

    16,361Voir sur GitHub↗

    Gensim is a natural language processing toolkit designed for large-scale text analysis and the training of semantic vector embeddings. It provides a framework for identifying latent thematic structures within document collections and calculating semantic similarity between text segments using unsupervised statistical algorithms. The project is distinguished by its ability to handle datasets that exceed available system memory through incremental corpus streaming, which processes documents one at a time from disk. It utilizes sparse vector representations and dictionary-based token mapping to

    Identifies latent thematic structures within document collections to categorize and summarize content.

    Pythondata-miningdata-sciencedocument-similarity
    Voir sur GitHub↗16,361
  • llmware-ai/llmwareAvatar de llmware-ai

    llmware-ai/llmware

    14,838Voir sur GitHub↗

    llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural lang

    Condenses long-form text into concise summaries or headlines based on specified length parameters.

    Python
    Voir sur GitHub↗14,838
  • jujumilk3/leaked-system-promptsAvatar de jujumilk3

    jujumilk3/leaked-system-prompts

    14,134Voir sur GitHub↗

    This project is a research-oriented repository that serves as a centralized database for system-level prompts and internal behavioral instructions extracted from various large language models. Its primary purpose is to provide a transparent, accessible reference for researchers and developers to study how artificial intelligence models are configured, constrained, and governed. The repository distinguishes itself by cataloging the hidden directives and operational guidelines that define model personas and safety boundaries. By archiving these instruction sets, it enables comparative analysis

    Summarizes complex model instructions into concise reference points.

    aidocumentllm
    Voir sur GitHub↗14,134
  • yandexdataschool/nlp_courseAvatar de yandexdataschool

    yandexdataschool/nlp_course

    10,591Voir sur GitHub↗

    YSDA course in Natural Language Processing

    Produces concise summaries of longer texts using extractive or abstractive methods.

    Jupyter Notebook
    Voir sur GitHub↗10,591
  • yusufkaraaslan/skill_seekersAvatar de yusufkaraaslan

    yusufkaraaslan/Skill_Seekers

    9,641Voir sur GitHub↗

    Skill Seekers is a toolset for generating large language model knowledge bases, featuring a multi-source content scraper and a dedicated RAG data pipeline. It extracts technical data from documentation, code, and video to create structured assets and configuration files for AI-powered IDE extensions. The project distinguishes itself through the ability to transform raw data into polished tutorials and specialized skills for AI plugin marketplaces. It utilizes abstract syntax tree parsing and optical character recognition to analyze GitHub repositories, PDFs, and video frames, converting these

    Summarizes concepts and identifies patterns using specialized workflow presets to improve content quality.

    Pythonai-toolsast-parserautomation
    Voir sur GitHub↗9,641
  • kreuzberg-dev/kreuzbergAvatar de kreuzberg-dev

    kreuzberg-dev/kreuzberg

    8,527Voir sur GitHub↗

    Kreuzberg is a document extraction engine that converts PDFs, Office files, images, and over 90 other formats into clean, structured text and metadata. It is built around a compiled Rust core that can be used as a native library, a command-line tool, a REST API server, or a WebAssembly module for browser-based processing. The system is designed to run entirely on self-hosted infrastructure, with no data leaving the user's environment. What distinguishes Kreuzberg is its breadth of integration surfaces and its pipeline architecture. It exposes extraction capabilities through native bindings fo

    Generates a prose summary of extracted content using a local TextRank or an LLM-powered abstractive backend.

    Rustdocument-intelligenceelixirffi
    Voir sur GitHub↗8,527
  • google/traxAvatar de google

    google/trax

    8,304Voir sur GitHub↗

    Trax is a deep learning framework and hardware-agnostic tensor engine designed for designing and training neural networks. It serves as a research tool providing high-level combinators for composing complex architectures, alongside a dedicated library for building transformer models and a toolkit for reinforcement learning. The framework is distinguished by its support for reversible and sparse transformer architectures, which reduce memory and computational overhead. It enables a single set of model instructions to execute across different hardware backends without changing the underlying co

    Supports condensing long-form text into shorter summaries using pre-trained transformer models.

    Python
    Voir sur GitHub↗8,304
  • tingsongyu/pytorch_tutorialAvatar de TingsongYu

    TingsongYu/PyTorch_Tutorial

    8,018Voir sur GitHub↗

    This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene

    Processes batches of documents by segmenting text and generating collective summaries using language models.

    Python
    Voir sur GitHub↗8,018
  • ymcui/chinese-llama-alpaca-2Avatar de ymcui

    ymcui/Chinese-LLaMA-Alpaca-2

    7,136Voir sur GitHub↗

    This project provides a Chinese large language model based on the LLaMA architecture. It is an instruction-tuned model optimized for natural language processing and multi-turn conversations in Chinese. The system includes a framework for parameter-efficient fine-tuning using low-rank adaptation and quantization to reduce memory requirements. It also implements retrieval augmented generation for local document question answering and supports long-context processing for sequences up to 64K tokens. The project covers a broad set of capabilities including supervised instruction tuning, reinforce

    Provides automated methods for condensing long-form text files into concise summaries.

    Python64kalpacaalpaca-2
    Voir sur GitHub↗7,136
  • hatchet-dev/hatchetAvatar de hatchet-dev

    hatchet-dev/hatchet

    6,622Voir sur GitHub↗

    Hatchet is an open-source durable workflow engine and task orchestration platform. It provides a framework for building and executing fault-tolerant, multi-step pipelines as directed acyclic graphs (DAGs), with automatic retries, scheduling, and real-time observability. The system is built around durable task checkpointing, which persists execution state after each step so work can resume from the last checkpoint after a worker crash or restart, and it supports event-driven task resumption that pauses a task until a matching external event arrives. The platform distinguishes itself through it

    Generates a short summary of a document's text by capturing the first words and including the total word count.

    Goconcurrencydagdistributed
    Voir sur GitHub↗6,622
  • tagspaces/tagspacesAvatar de tagspaces

    tagspaces/tagspaces

    4,935Voir sur GitHub↗

    TagSpaces is an offline-first file tagging and organization platform that lets you manage local files with portable metadata stored directly in filenames or sidecar JSON files, eliminating the need for a central database. It functions as a full-text file search engine, a Kanban board file organizer, a local AI file assistant, an S3-compatible cloud file manager, and a web clipper and bookmark manager, all within a single application. The project distinguishes itself through a local-first architecture where all file operations, indexing, and AI processing run entirely on the device, with cloud

    TagSpaces produces concise summaries of text files, legal contracts, or research papers by processing them through an offline AI model.

    TypeScriptelectronjavascriptnote-taking
    Voir sur GitHub↗4,935
  • tingsongyu/pytorch-tutorial-2ndAvatar de TingsongYu

    TingsongYu/PyTorch-Tutorial-2nd

    4,555Voir sur GitHub↗

    This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It covers the fundamental building blocks of deep learning, including tensor manipulation, automatic differentiation, and the construction of modular neural network components. The repository serves as a technical guide for several specialized domains. It provides implementation details for computer vision tasks such as image classification, object detection, and semantic segmentation, as well as natural language processing workflows involving transformers, recurrent networks, and gen

    Provides techniques for generating a single consolidated summary from multiple source documents.

    Jupyter Notebookcomputer-visiondeepsortdiffusion-models
    Voir sur GitHub↗4,555
  • jabref/jabrefAvatar de JabRef

    JabRef/jabref

    4,373Voir sur GitHub↗

    This project is a desktop-based bibliographic reference manager designed to organize academic research libraries and automate citation workflows. It functions as a research assistant that integrates directly with word processors and text editors, enabling users to insert and format references while writing. The application is built on a Java-based portable runtime, allowing it to operate as a self-contained tool that stores preferences and data in local configuration files. The platform distinguishes itself through a modular plugin architecture and a commitment to human-readable, text-based f

    Generates concise overviews of attached PDF files to accelerate the literature review process.

    Javaacademiaacademic-publicationsai
    Voir sur GitHub↗4,373
  • johnsnowlabs/spark-nlpAvatar de JohnSnowLabs

    JohnSnowLabs/spark-nlp

    4,135Voir sur GitHub↗

    Spark NLP est une boîte à outils pour l'analyse de texte évolutive et l'apprentissage automatique construite sur le framework de calcul distribué Apache Spark. Il fournit un framework d'apprentissage automatique multimodal et un système de pipeline distribué pour séquencer les annotateurs afin de traiter des données linguistiques à grande échelle. La bibliothèque inclut un processeur de texte transformer pour générer des embeddings vectoriels contextuels et un moteur d'inférence dédié pour gérer les grands modèles de langage. Le projet se distingue par sa capacité à traiter des types de données hétérogènes, y compris le texte, l'audio et les images, au sein d'une architecture vision-langage unifiée. Il prend en charge des capacités avancées d'IA générative telles que le prompt engineering, l'extraction d'entités structurées avec sortie JSON contrainte, et l'inférence locale pour éliminer la latence réseau. De plus, il fournit des outils pour la traduction inter-langues et la classification zero-shot à travers les modalités texte et image. Le framework couvre un large éventail de capacités, y compris l'entraînement de modèles supervisés pour la reconnaissance d'entités et l'analyse de sentiment, ainsi que la réponse aux questions extractive et la synthèse de documents. Il intègre la prise en charge des bases de données vectorielles pour la recherche de similarité et offre une infrastructure pour l'accélération GPU et la gestion du cycle de vie des modèles via un registre centralisé. La boîte à outils permet la distribution de modèles et de pipelines personnalisés via un dépôt public et prend en charge le déploiement de modèles via des API REST.

    Condenses long-form documents into concise summaries while preserving main ideas.

    Scala
    Voir sur GitHub↗4,135
  • langroid/langroidAvatar de langroid

    langroid/langroid

    3,894Voir sur GitHub↗

    Langroid is a multi-agent orchestration framework and tool integration suite designed for building complex AI applications. It serves as a multi-modal integration layer that connects diverse local and remote language models with an agentic retrieval-augmented generation system. The project distinguishes itself through a collaborative message-exchange paradigm, allowing specialized agents to delegate tasks hierarchically and coordinate via structured communication. It features an advanced state management system for conversational AI, including the ability to rewind and prune conversation hist

    Aggregates content from ingested documents to produce concise summaries.

    Pythonagentsaichatgpt
    Voir sur GitHub↗3,894
  • ucbepic/docetlAvatar de ucbepic

    ucbepic/docetl

    3,597Voir sur GitHub↗

    docetl is an AI-powered document ETL tool and map-reduce orchestrator designed to transform large collections of unstructured documents into structured, queryable tables using language models. It provides a declarative pipeline framework for extracting, cleaning, and transforming data from sources such as PDFs and text files into predefined schemas. The project distinguishes itself through a semantic data integration suite that enables joining datasets and resolving duplicate entities based on embedding-based similarity. It includes an interactive prompt playground for developing and optimizi

    Condenses key information from multiple documents into structured summaries using a reduction process.

    Pythonagentsdatadata-pipelines
    Voir sur GitHub↗3,597
  • avinashkranjan/amazing-python-scriptsAvatar de avinashkranjan

    avinashkranjan/Amazing-Python-Scripts

    3,425Voir sur GitHub↗

    This is a collection of Python automation scripts and utility tools designed to handle repetitive technical tasks, system administration, and developer workflows. The project serves as a suite for task automation, data utility, and web automation. The collection includes specialized tools for multimedia processing, such as optical character recognition for extracting text from images, speech-to-text conversion, and real-time face and human body detection. It also features web scraping and monitoring capabilities to track product prices, fetch external API content, and automate interactions wi

    Includes functionality to generate concise sentence-level summaries from longer text documents.

    Jupyter Notebookartificial-intelligencehacktoberfestmachine-learning
    Voir sur GitHub↗3,425
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Explorer les sous-tags

  • Multi-Document SummarizationGenerating a single consolidated summary from multiple source documents. **Distinct from Document Summarization:** Specifically handles the aggregation of information across a batch of files rather than a single long document.