Popular GitHub Repositories for Abstractive

Deep Reinforcement Learning For Sequence to Sequence Models

Code for ACL 2018 paper: "Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting. Chen and Bansal"

Multiple implementations for abstractive text summurization , using google colab

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

Abstractive summarisation using Bert as encoder and Transformer Decoder

A Abstractive Summarization Implementation with Transformer and Pointer-generator

Pointer-generator reinforced seq2seq summarization in PyTorch

ACL 2022: BRIO: Bringing Order to Abstractive Summarization

This repository contains the code, data, and models of the paper titled "XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages" published in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021.

Models for automatic abstractive summarization

Abstractive Text Summarization using Transformer

Implementation of abstractive summarization using LSTM in the encoder-decoder architecture with local attention.

Source code for the NAACL 2019 paper "SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression"

Official codes for the paper: A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss.

[EMNLP 2019] Mixture Content Selection for Diverse Sequence Generation (Question Generation / Abstractive Summarization)

FactSumm: Factual Consistency Scorer for Abstractive Summarization

ACL 2020 Unsupervised Opinion Summarization as Copycat-Review Generation

Source codes for the paper "Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization"

Code for EMNLP 2021 paper "Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization"

A tool to automatically summarize documents abstractively using the BART or PreSumm Machine Learning Model.