🔍 Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, Falcon and alike). Haystack offers production-ready tools to quickly build complex question answering, semantic search, text generation applications, and more.

Module for automatic summarization of text documents and HTML pages.

Python implementation of TextRank algorithms ("textgraphs") for phrase extraction

Gathers machine learning and Tensorflow deep learning models for NLP problems, 1.13 < Tensorflow < 2.0

A modular RL library to fine-tune language models to human preferences

Returns latest research results by crawling arxiv papers and summarizing abstracts. Helps you stay afloat with so many new papers everyday.

TextRank implementation for Python 3.

Summarization Papers

pytorch implementation of "Get To The Point: Summarization with Pointer-Generator Networks"

Converse with book - Built with GPT-3

Basic statistics for Julia

Automatically generate headlines to short articles

Korean BART

Abstractive summarisation using Bert as encoder and Transformer Decoder

Models to perform neural summarization (extractive and abstractive) using machine learning transformers and a tool to convert abstractive summarization datasets to the extractive task.

Pointer-generator reinforced seq2seq summarization in PyTorch

simpleT5 is built on top of PyTorch-lightning⚡️ and Transformers🤗 that lets you quickly train your T5 models.

Text2Text: Crosslingual NLP/G toolkit

Large-scale multi-document summarization dataset and code

Efficient Retrieval Augmentation and Generation Framework