Easy to use extractive text summarization with BERT
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Updated
Jun 12, 2023 - Python
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
Datasets I have created for scientific summarization, and a trained BertSum model
Text summarization starting from scratch.
Abstractive and Extractive Text summarization using Transformers.
Automagically generates summaries from html or text.
Code and Data Repo for COLING'22 paper "Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization"
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
Tensorflow implementation of SummaRuNNer
Automatic generation of reviews of scientific papers
Source based extractive summarizer web-app and chatbot.
SUMPUBMED: Summarization Dataset of PubMed Scientific Article
Let AI create the notes of your Teams Meeting
Use-cases of Hugging Face's BERT (e.g. paraphrase generation, unsupervised extractive summarization).
Ultra-fast, spookily accurate text summarizer that works on any language
Scripts for an upcoming blog "Extractive vs. Abstractive Summarization" for RaRe Technologies.
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.
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