Python wrapper for evaluating summarization quality by ROUGE package

Document Search Engine Tool

Code for the paper "Efficient Adaption of Pretrained Transformers for Abstractive Summarization"

Ally – AI Contract Assistant is a Word plugin using Azure OpenAI for contract analysis, real-time Q&A, and auto-markup. It helps legal professionals save time and ensure accuracy in reviews.

Extractive Document Summarization Based on Convolutional Neural Networks

A python application for summarizing Slack threads

[ACL2020] Unsupervised Opinion Summarization with Noising and Denoising

[NAACL2018] Entity Commonsense Representation for Neural Abstractive Summarization

문서 요약 논문 정리

LoRA supervised fine-tuning, RLHF (PPO) and RAG with llama-3-8B on the TLDR summarization dataset

Implementation for multi-document query-based abstractive summarisation

Summarize documents based on content extracted via Rosette API

Turn PDF into Notes in seconds📝

Automatic Extractive Text Summarization using TF-IDF Frequency Analysis. This is a Node.js web application using Express.js on the server side.

This project demonstrates summarizing large documents with Azure OpenAI and Durable Functions, using a Fan-out/Fan-in pattern to process sections in parallel and compile a cohesive summary. It ensures scalable and efficient document handling with Azure services.

Extract Amazon customer reviews of select products via a third-party educational web scrapper, analyze them with LLM, and display concise summaries using an interactive user interface.

ClearClause: A Legal AI Assistant using Google Gemini API for QnA, summaries, translations, and semantic search across your legal documents.

Simple Query Based Document Summarization

Muisca: Modelo Unificado de Inteligencia Supervisada para la Computación y Aplicación. Una herramienta Streamlit para resumir y hacer preguntas sobre documentos en PDF y TXT utilizando modelos de lenguaje de última generación.

agentnovax-api-rag-springboot-ollama-pgvector is a Spring Boot-based API that combines Retrieval-Augmented Generation (RAG) with the Ollama language model API and pgvector for vector search in PostgreSQL. It enables scalable, intelligent AI solutions for applications like recommendation systems, chatbots, and context-aware responses.