Easy to use extractive text summarization with BERT
-
Updated
Jun 12, 2023 - Python
Easy to use extractive text summarization with BERT
The PyTorch Implementation of SummaRuNNer
Models to perform neural summarization (extractive and abstractive) using machine learning transformers and a tool to convert abstractive summarization datasets to the extractive task.
Lecture summarization with BERT
a contextual, biasable, word-or-sentence-or-paragraph extractive summarizer powered by the latest in text embeddings (Bert, Universal Sentence Encoder, Flair)
Automagically generates summaries from html or text.
Text summarization starting from scratch.
Code for ACL 2022 paper on the topic of long document summarization: MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes
Datasets I have created for scientific summarization, and a trained BertSum model
Scripts for an upcoming blog "Extractive vs. Abstractive Summarization" for RaRe Technologies.
This is an extractive based text summarization.
A system capable of converting Nepali speech to text and generate summary of text
Tensorflow implementation of SummaRuNNer
Abstractive and Extractive Text summarization using Transformers.
Functions for creating and analyzing word co-occurrence networks in Python and R
Extractive Multi-document Summarization
Pointer network for extractive summarization
This module summarizes any text using extractive summarization, an unsupervised technique.
unsupervised graph-based ranking model
Automatic generation of reviews of scientific papers
Add a description, image, and links to the extractive-summarization topic page so that developers can more easily learn about it.
To associate your repository with the extractive-summarization topic, visit your repo's landing page and select "manage topics."