Easy to use extractive text summarization with BERT

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

The PyTorch Implementation of SummaRuNNer

a contextual, biasable, word-or-sentence-or-paragraph extractive summarizer powered by the latest in text embeddings (Bert, Universal Sentence Encoder, Flair)

Lecture summarization with BERT

[COLING 2022] Code and Data Repo for Paper "Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization"

Tensorflow implementation of SummaRuNNer

Let AI create the notes of your Teams Meeting

An AI-powered tool for transcribing, summarizing, and creating smart clips from video and audio content.

Simple and clean Python implementation of TextRank as per seminal paper by Rada Mihalcea and Paul Tarau. This implementation performs both keyword extraction as well as text summarization.

Shortens a paragraph of text with ChatGPT, using successive rounds of word-level extractive summarization.

Text Summarization with Pretrained Encoders

A script to process the ArXiv-PubMed dataset.

This project is a versatile Optical Character Recognition (OCR) and translation tool designed to process both images and PDF documents and leveraging NLP to translate the text into different languages and give users an option to summarize the contents of a document in their prefered language

Pointer network for extractive summarization

Simple Extractive Text Summarization using SpaCy, using a frequency model

KoBertSum: AI Hub 문서요약 텍스트에서 학습된 BertSum 기반의 한국어 요약 모델

A BERT-based text summarization tool

This is an extractive based text summarization.

Feature extraction scripts for the DISCOSUMO project, to be used for extractive summarization of discussion threads.